text:
Talk:Issues in Interdisciplinarity 2020-21/Power in Contact Tracing Apps

=Feedback= This section provides a space for group members to provide feedback and suggestions for improvement. It is organised in the same structure as the wikibook.

Introduction
The introduction to the concept of contact-tracing apps itself looks great. We discussed that it would also be helpful to introduce the concept of power in this section and how it applies to the discussion to ensure that we do not lose focus of the main interdisciplinary issue. Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

The implementation of contact-tracing apps is a complex interdisciplinary issue relying on collaboration between experts across disciplines. Naturally, power dynamics between disciplinary fields come into play as different actors attempt to influence the design of the app and how it is implemented in society. This is particularly relevant to the privacy versus effectiveness debate (ie. to what extent can privacy be compromised to provide more personal data and therefore enable a more effective track and trace system?).

We have identified three disciplines, all of which play a key role in this debate and contribute to interdisciplinarhy tension as contradictory perspectives arise on how best to approach the trade-off between privacy and effectiveness:

1. Technology/ Computer Science: scientists (people working directly on the technology of the apps) are generally held in high authority and their "expert" advice has the power to influence the privacy vs effectiveness debate through the suggestion that certain compromises on privacy are necessary to make the technology work effectively. Privacy is not likey to be their main concern since they will generally be looking at the issue from a purely technological/pragmatic perspective.

2. Sociology: a sociological perspective on the issue holds significant power, as policy-makers' understanding of how citizens will respond to the app factors into decision-making. Trust is a key factor here.

3. Law: law obviously holds power - governments cannot take actions that do not abide by the law. Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Hello, I made a few small edits on the introduction and cut some sentences to make it flow better and introduce the interdisciplinarity tension in a more straightforward way. Let me know if there are any other changes you would make. --Fantomasque (discuss • contribs) 15:39, 12 December 2020 (UTC)

part to be removed and added to the main paragraphs about each discipline
Currently there is a battle around the point at which the development and effectiveness of contact tracing apps meet the protection of digital rights and privacy.

On one side of this debate, many argue from a scientific point of view. Research efforts, advancements in technology to combat the pandemic or any other health threats, are restrained by limitations on privacy and accessing certain data despite their purpose of improving safety and defense. In regards to epidemiological research, there aren’t many other resources scientists can base their studies on other than population-based data. Thus, it is an important issue from the scientific point of view as this takes a major toll on the effectiveness of any measure implemented. If we want contact tracing apps to be efficient they have to be almost omnipresent because if only a small proportion of the population uses them, then they are pointless and can even be damaging: not only would the data be incorrect but it could lead to a false sense of safety or danger. In spite of all this, application developers and researchers are faced with legal as well as sociological drawbacks.

On the other side of the debate, some believe that contact tracing apps are a breach of our digital rights. This argument is established through disciplines such as law and sociology. Individuals value the protection of their right to privacy. Thus many people apprehend, through the implementation of these apps, the type of data they will collect and fear the risk of mission creep. However, despite this sentiment being universal, it is not always regarded the same way by policy makers. Indeed, governments rely mainly on the response of the population to determine their approach when implementing the app. In Asia, countries tend to impose the apps on its citizens, as well as using card transaction records and surveillance camera footage. This helps greatly with containing the spread of the virus, but has a serious cost on people’s rights to privacy. European countries are considered more « privacy-conscious » which also comes with a lot of skepticism from the people towards their government and the use of their data. Consequently, very low rates of usage of these applications have been observed, culminating at 34% in Ireland.

In that sense, a dynamic of competition appears between the two approaches. --Fantomasque (discuss • contribs) 15:39, 12 December 2020 (UTC)

Technology
The latent dimension of power seems the most relevant here (see the table from the lectures). We hold scientists in high regard and the scientific perspective on any issue would generally be considered relatively authoritative. Consider Borris Johnson's claims that the UK policy-makers are "following the science" - science has the power to justify government actions, and this is especially relevant to the use of personal data in contact tracing apps.Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Thus, the scientific perspective "excersises power over [decision-makers] by influencing, shaping or determining [citizens' and governments'] very wants" (adapted quote on latent dimension of power from the lecture slides). Our "wants" in relation to the privacy of our personal data may change in response to learning about the necessity of this data for creating effective technology.Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Thanks for this! I found some research articles about the power scientists and experts in a particular field like: https://www.jstor.org/stable/284554?seq=1 and a book about 'technology triumphalism' called 'Creativity, Problem Solving, and Aesthetics in Engineering: Today's Engineers Turning Dreams Into Reality'. This book also mentions how sometimes scientists think that technology can solve all problems as long as they can convince the public that they are right. I think this is a clear example of latent power which I will definitely mention in the technology section! PerpetualMisfit (discuss • contribs) 14:15, 8 December 2020 (UTC)

Sociology
A useful point to make here might be that power manifests as resource dependency (look into Emerson's research, as cited in the lectures), where citizens' trust is the resource which governments and policy-makers rely on (ie. without trust in the ability of the app to respect their privacy rights, citizens will not download the app, and the entire track and trace system will therefore be rendered ineffective). Therefore, citizens have power over officials, forcing them to consider privacy concerns as an integral part of the process of implementing contact-tracing apps. Ridumdepiro (discuss • contribs) 16:24, 7 December 2020 (UTC)

Highlight the differences in different countries' apps as evidence for the significant role a sociological understanding of the issue plays in what the apps look like and how they are implemented. The shift from centralised to de-centralised is also really helpful evidence we discussed using here. Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Also, we could circle back to the scientific perspective here for a more well-rounded consideration of the issue - promoting an understanding/ awareness of the science definitely influences citizens themselves, making them more accepting of breaches of privacy because they understand its necessity and respect the authority of science. This in turn allows governments to compromise more on privacy by harnessing the power of science. Ridumdepiro (discuss • contribs) 17:20, 7 December 2020 (UTC)

Yes I agree with all the points here!! Another thing to add on is how sociologists or people interested in promoting data privacy is how they speak the language of scientists as they talk about the effectiveness of the app if no one downloads it if they think it's not private enough. PerpetualMisfit (discuss • contribs) 14:17, 8 December 2020 (UTC)

Definitely! This is a really good point because language has so much power in terms of influencung our understanding, and it is a tool that can be used to harness the authority of science. We should definitely refer to the language that policy-makers and the media use to talk about the issue. Ridumdepiro (discuss • contribs) 16:09, 8 December 2020 (UTC)

I made the argument that sociology has power, but I'm worried that maybe this doesn't make sense, especially if we are talking power in terms of resource dependency, since it's the citizens that hold the resource of trust, not the discipline of sociology itself. Yes, a consideration of the issue from a sociological perspective has the capacity to influence the debate, but I may need to revisit this argument and clarify a bit. Ridumdepiro (discuss • contribs) 16:48, 8 December 2020 (UTC)

I agree that trust is a form of resource that scientists are dependent on for their contact tracing solutions to work.

Edited the part on sociology to change the flow of the section and reduce word count! Let me know what you think. PerpetualMisfit (discuss • contribs) 16:00, 12 December 2020 (UTC)

I just read through this new version of the sociology paragraph - I think the more condensed structure works well and I only made a small edit on the sentence about sociologists wielding power in terms of resource dependency. HotelBudapest (discuss • contribs) 16:10, 12 December 2020 (UTC)

Law
Policy-makers are restricted by legal considerations. Even if the sociological perspective suggests that citizens would be prepared to accept certain breaches of privacy, the legal perspective represents another obstactle, suggesting that law as a discipline holds significant power over the debate. Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Only to a certain extent though, because law is contestable and open to interpreatation. The legal concepts of necessity and proportionality and key here - as long as you can prove that the use of this data is necessary, it does not breach the right to protection of personal data (eg. according to EU law). This is where the scientific perspective has power again, because it is up to them to provide the evidence for this necessity... Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Like the sociological perspective, it also depends on national context. Laws differ by country - look into South Korean government's ability to overturn privacy laws in times of crisis as evidence. Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Power as decision-making, overt coersion? (Robert Dahl)

I've written the law section, but I'm worried it doesn't read very coherently. Would you mind taking a look and letting me know what you think? I think we may need some clarification on the concept of necessity and its relevance to our argument. Ridumdepiro (discuss • contribs) 15:28, 12 December 2020 (UTC)

Conclusion
Governments have decision-making power (overt), but these decisions are influenced by their consideration of legal constraints, as well as by an understanding of the sociological factors that will influences citizens' response to the system within a particular society. Ridumdepiro (discuss • contribs) 16:02, 8 December 2020 (UTC)

Does this lead to a push-and-pull that results in a relatively balanced power dynamic between privacy and effectiveness, as both sides of the debate have significant representation?Obviously the resulting balance of power differs between societies, but ultimately is a balance between what is necessary for an effective contact-tracing system and what citizens are prepared to accept reached? There has been strong criticism of the NHS app, for example, suggesting that citizens may be prepared to compromise for personal data than the law currently permits, so maybe not... leave this an open question in the conclusion? Ridumdepiro (discuss • contribs) 17:20, 7 December 2020 (UTC)

I edited the conclusion to cut down on word count, do let me know what you think! PerpetualMisfit (discuss • contribs) 04:26, 12 December 2020 (UTC)

Thank you, it looks great! I don't think we've lost any of the original points at all. Ridumdepiro (discuss • contribs) 15:30, 12 December 2020 (UTC)

21 Nov
Wikibooks Topic Ideas:
 * Abortion
 * Power in gun control
 * OJ Simpson
 * Power in Education
 * Feminism
 * History of race

23 Nov
Met in person to brainstorm the topic of contact-tracing apps.

 * Narrowed the topic down to the privacy vs. effectiveness debate surrounding contact tracing apps
 * Attemped to identify interdisciplinary tension: the power dynamics between disciplines as they collaborate on interdisciplinary apps and navigate the comprimise between the effectiveness of the apps (encouraged by computer science, policy-making...) and the respect for privacy concerns (encouraged by law, ethics...)
 * Discussed truth and power as relevant interdisciplinary issues
 * Decided on power as the most relevant issue to the topic
 * Planned to meet again after looking through sources to narrow down our ideas and allocate responsibilities to group members

27 Nov

 * Central argument/conflict: how much 'information' should these contact tracing apps 'take'
 * the more information, the more effective these apps will be (and as a result the more effective they will be in reducing the spread of the virus)
 * on the other hand, this leads to issues surrounding privacy (which extends to the disciplines of law and even philosophy/sociology)
 * Power looks to be the best issue by which to tackle this topic/argument as the way these apps are implemented comes down to finding a balance between these two factors: privacy and effectiveness. In other words, there is a power dynamic between these two factors and considering various disciplines can help us understand the importance of both.

5 Dec
Analyzing articles to identify the disciplinary tensions in the problem about designing contact tracing apps that don't compromise on privacy but are effective in aiding contact tracing

The Google Apple (exposure notification) protocol Decentralized vs centralized protocol Computer science/technology, law, sociology Quotes from academics from different fields Can mobile contact-tracing apps help lift lockdown? Different priorities
 * Identify a research question or problem that would clearly benefit from an interdisciplinary approach
 * Identify the two or three disciplinary perspectives that could profitably combine
 * Identify some tension between these disciplinary perspectives
 * Attribute that tension to an interdisciplinary issue

To do:
 * Move resources and summaries from Google Docs to Resources section in the discussion page

7th Dec

 * Discussed our contact tracing case study more in terms of power (partly as a result of having come out of an ATK seminar about truth, evidence and power).
 * Pointed out an issue with the rigid structure of splitting this case study into the disciplines as currently the sociology section might be leaning a bit towards law. If we want to maintain this structure, must ensure that sociology section is more focused on truth and language.
 * Started to draw up conclusions about how the shift in popularity from centralised to decentralised system is a result of the growing debate around (and importance placed on) privacy
 * Mentioned how law changes have been suggested to accommodate for privacy
 * Above two bullet points show how sociology and digital ethics are powerful and have influence
 * These comments on power can be interspersed thoughout the writing and then tied up together in the final conclusion (which we plan to start writing on Wednesday).

9th Dec
Detail tension between them Disciplinary tension can be a good thing, helps reach a balance (scientists are frustrated, but they need that interdisciplinary challenge to keep them in check because privacy is also important, same goes for everything else). Middle ground that works for everyone Different balance for each society (sociology) Too simplistic to see it as a ‘battle’, they are pushing people to consider more perspectives Privacy- safe guarding against future problems Push to a better solution It’s a kind of cycle, a constant push & pull between privacy and effectiveness so that the debate stays in balance
 * Went through the whole chapter after our individual inputs to integrate the various parts
 * For computer science, decided to focus more on power and how that power was at first with the centralized version
 * For sociology, wanted to elaborate more about how power was executed through power as a resource and power in using the language of computer scientists (speaking in terms of effectiveness and quantifying data)
 * For law, elaborate on how laws have overt power as they must be followed. However, some countries have unique laws that allow them to bypass privacy and data collection laws during a public health crisis
 * Made a plan for the conclusion:

11th Dec

 * Most of the writing was finished by this point, but we were significantly over the word count and a lot of the writing was quite repetitive and rambling. The aim of this meeting was mainly to clean things up and make the structure more coherent.
 * Made minor edits to the introduction and technology sections
 * Reviewed the newly written conclusion
 * Significantly re-structured the section on sociology to clarify the relevance of trust and make it flow more coherently
 * Changed the section on law to include less information, due to word limit constraints

12th Dec

 * Significantly re-structured and re-worded the law section to make it more coherent
 * Went over new structure for the sociology section and after a few minor edits agreed to keep it
 * Made minor changes of phrasing to help reduce the word count
 * Agreed on a standard way of formatting our citations because it was brought up that everyone's citations looked slightly different
 * Added some final citations
 * Transferred edits from the google doc to the wikibook

Structure
We came up with this structure during the Zoom meeting on 5th Dec

Introduction
Introduce topic of contact tracing apps (v. brief outline of how they work) Outline the privacy vs effectiveness debate Privacy: law, digital rights Effectiveness: computer science/ technology Decision makers: governments/policy makers

Computer Science/ Technology
Decentralized vs centralized model (conflict) Quotes from computer scientists,

Law/Digital rights
In general, more hardline stance

Sociopolitical context
Whether it will be accepted by the citizens (understanding of how people will react influences government decisions -> sociology has power?) Use how different the apps are in different countries as evidence for this

Analysis/Conclusion
Consensus: understand this is a pandemic and if it is necessary and can be justified, normal conventions on privacy can be violated Power in balance in different approaches, analyse what countries have chosen to completed disregard privacy Blatantly disregarding privacy Scientists and law academics agree GPS tracking (Israel) Other countries, like in the EU, chose for an approach more centred around privacy, even if it isn’t the most effective

=Resources=

Computer science/Technology
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing (March 31, 2020) A research article which calls for the implementation of contact tracing apps to detect and isolate coronavirus cases due asymptomatic and pre-symptomatic transmission.

Proximity: a recipe to break the outbreak A research paper that details a smartphone application for contact tracing and explains why such an app is vital to contain an outbreak and how Bluetooth is the most suitable technology.

Law
Guidance from the EU data protection supervisor: https://edps.europa.eu/data-protection/our-work/subjects/necessity-proportionality_en
 * outlines the legal concepts of necessity and proportionality
 * Obviously the law holds a certain authority (power) - no-one, even governments, can technically break the law. So if something is deemed “true” within this discipline (i.e. enshrined in law) it definitely has a significant power
 * But the law is open to interpretation - as long as we can justify that the use of personal data is necessary, its use is deemed not to be infringing upon people’s fundamental rights.
 * “Objective evidence” - what does this mean? This is where Law loses some of its power and the power falls into the scientists’ hands - if they can provide evidence to prove that the data they are using is necessary to protect public health etc., the law can’t stop them.
 * The source mentions determining the “proportionality of the limitation” - but how do we quantify this “proportionality”?

Keep in mind that this is specific to EU law. Academics contributed an article to the European Law Blog: https://europeanlawblog.eu/2020/05/04/covid-19-a-new-struggle-over-privacy-data-protection-and-human-rights/
 * Stresses the same concepts of necessity and proportionality (“The processing is lawful where it is necessary to ‘protect the vital interests of the data subject or of another natural person (Article 6(1)(d) of GDPR)”)
 * Examines the hypothesis that data protection rules slow down the response to the Covid-19 pandemic.

The EU GDPR law can be accessed online here: https://eur-lex.europa.eu/eli/reg/2016/679/oj
 * Obviously not an academic paper, but might be useful to cite certain points (eg. the one quoted by the source above)

A site outlining global laws relevant to personal privacy and data protection in the context of contact-tracing apps: https://uk.practicallaw.thomsonreuters.com/w-024-5356?originationContext=document&transitionType=DocumentItem&contextData=(sc.Default)&firstPage=true

The gov uk privacy notice on the NHS app could be used as evidence for how much power the law holds in strengthening the privacy side of the debate: https://www.gov.uk/government/publications/nhs-covid-19-app-privacy-information/nhs-test-and-trace-app-early-adopter-trial-august-2020-privacy-notice

Legal academics across some UK universities have published a proposed bill intended to safeguard privacy in light of the introduction of contact-tracing apps: https://osf.io/preprints/lawarxiv/yc6xu/

Contains some quotes from legal experts and computer scientists that shed light about how the different disciplines feel about a contact tracing app: https://www.bbc.com/future/article/20200415-covid-19-could-bluetooth-contact-tracing-end-lockdown-early

Sociopolitical/Political Science/Policy
An academic paper that discusses various privacy models underpinning contact tracing apps. It also highlights the unique sociopolitical context in Singapore that has resulted in broad acceptance by its citizens. Under the section “3.3 Privacy from the Authorities” they talk about the sociopolitical context https://arxiv.org/pdf/2003.11511.pdf

Article on policy making during crisis, focused on political leaders respond to expertise from scientific advisors: https://www.bmj.com/content/371/bmj.m4039
 * Implies that although the scientists are included in the discussion, they have no real power over the policies that are made because of the influence of political factors
 * Furthermore, governments can draw on catchphrases like “following the science” and “listening to the experts” to lend authority to their own decisions and increase their own power

paper that looks at the relationship between science and politics in the context of the UK’s response to the pandemic: https://www.nottingham.ac.uk/vision/c19-needs-good-politics-too The main point is that science has its limitations and cannot be treated as a rulebook for politicians to follow step-by-step. It is constantly evolving, and provides empirical evidence is subject to different interpretations. (we are giving science a level of power that it may not deserve) These limitations must be acknowledged, if we are to have a real debate between privacy/effectiveness, otherwise it allows politicians to shield responsibility for their decisions.
 * The scope of this paper is wider (ie. pandemic response in general, not contact-tracing apps specifically) but can still be applied to our context

Journal article on “Citizens’ attitudes to contact-tracing apps”: https://www.cambridge.org/core/journals/journal-of-experimental-political-science/article/citizens-attitudes-to-contact-tracing-apps/F9B8B8CFE051E6D89C3C9ADD6DF76019

A table with the different contact tracing apps of different countries and whether they meet certain conditions to ensure privacy: https://www.technologyreview.com/2020/05/07/1000961/launching-mittr-covid-tracing-tracker/

https://reutersinstitute.politics.ox.ac.uk/our-research/privacy-probity-and-public-interest : research paper on how far the media can go when invading people’s privacy. Useful to mention when talking about how much society values privacy (especially in Europe)

https://plato.stanford.edu/entries/it-privacy/ : A more philosophical approach to what privacy mean in a society and how it relates to the collection of data and technology in general.

https://www.digitalhealth.net/2020/10/major-questions-unanswered-about-effectiveness-of-nhs-covid-19-app/ : article that can help illustrate a form of skepticism towards the NHS app in the UK, and contact tracing apps in Europe in general (could be useful for sociological approach) https://www.bbc.co.uk/news/technology-53466471

https://www.oii.ox.ac.uk/blog/beyond-contact-tracing-apps-how-trust-shapes-e-governance/ = research paper on the important of trust in government when sharing data, using the example of Finland or Ukraine


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Issues in Interdisciplinarity 2020-21/Power in Contact Tracing Apps

=Power in Contact Tracing Apps=

Introduction
Contact tracing apps facilitate the identification of people that have come into contact with someone that has tested positive for COVID-19. Different countries have created different versions of this app, but this chapter focuses on apps that use Bluetooth to exchange signals with other phones with the same app.

This chapter deals specifically with the power dynamics that emerge between disciplines as they wrestle for influence over the design of contact-tracing apps. It identifies three main disciplines which contribute to this interdisciplinary tension as contradictory perspectives arise on how best to approach the trade-off between privacy and effectiveness: law, technology and sociology.

Law
Law has the most obvious overt power in this debate as those involved in the development of contact-tracing apps, from computer scientists to government officials, must act in accordance with the legal constraints on their work. This power is explicit as the demands for privacy protections are clearly laid out in documents such as the European Union's (EU) General Data Protection Regulations (GDPR).

However, the power of law is limited because the law is open to challenge. For example, the GDPR states that the processing of personal data can be lawful when it is strictly necessary for the purpose it serves, in this case reducing COVID-19 transmission rates. Furthermore, some countries have unique laws that grant exceptions to privacy laws during public health crises, such as the Infectious Diseases Control and Prevention Act of South Korea and the Infectious Diseases Act of Singapore. As what is deemed a necessity and a public health crisis is open to interpretation, there is a possibility for the overt power of law to shift to other parties who can prove that certain compromises on data privacy are necessary.

Technology
In policy making, technical expertise from scientists is widely regarded as a source of power that can influence policy decisions. The idea that modern digital technology can solve all societal problems has been growing rapidly in the field of technology, with the terms “solutionism” and “technology triumphalism” being created to criticize this idea. The manifestation of this can be seen in how scientists were quick to advocate for digital solutions, such as contact tracing apps, to quickly detect and isolate potential coronavirus cases as manual contact tracing was too slow and limited.

The crux of the debate on contact tracing apps has to do with whether apps should utilize a decentralised or centralised model. Under a centralised model, the anonymised data gathered by logging interactions with other users is uploaded to a remote server when the user tests positive from COVID-19. Under a decentralised model, this process is carried out on the mobile device itself and the records are not revealed to anyone, thus ensuring a higher level of privacy than the centralised model.

As the first apps were developed, most attention seemed to be on the centralised model, with a large collaboration of scientists from at least eight European countries starting the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) project. Proponents of the centralised model argued that the centralised collection of data would allow public health authorities to provide epidemiological insights like the location of clusters and which types of areas are prone to becoming clusters. It would also prevent individuals from falsely declaring themselves infected, making the data more accurate. As scientists convinced governments that this was vital to combat the spread of the virus, countries such as France and the UK started to develop apps based on the centralised model. This example of latent power, where one party exercises power over another by influencing what they think they need, led governments to exert their power. For example in April 2020, the government of France called on Apple and Google to relax the strict limits on what data their app can collect.

Sociology
Ensuring that privacy standards are maintained is a key consideration within sociology and therefore many sociologists have been weighing in on the debate between centralised and decentralised apps.

Sociologists believe that there is a major breach of privacy with the centralised model and the risk of mission creep could result in large scale state surveillance. They argue that for the app to be effective, a sufficient percentage of the population must accept the privacy tradeoffs and download the app. If these tradeoffs are too severe, the download rates of the app would be low. This can be seen in some countries utilizing the centralized model, such as France, where less than 10% of the population had downloaded the app as of October 2020.

As public trust is the resource that policymakers depend on to establish effective contact tracing systems, this relationship between public trust and policymakers can be seen as resource dependence. This form of power was exercised in April 2020, when over 300 academics from 26 countries signed a letter criticising the centralised model. They pushed for the more privacy-preserving decentralised version and argued that with this model, citizens would be less resistant to download the app, hence increasing its effectiveness. By explaining the dependency of the effectiveness of the app on public trust and thus preserving privacy, sociologists can influence the actions of policymakers. The impact of this power can be seen by the significant drop in popularity of the centralised model, with the UK switching from a centralised to a decentralised model and the withdrawn support for PPEP-PT in favor of it’s decentralised competitor.

However, this power is limited depending on the unique sociopolitical context of each country, which determines the level of trust people have in government. For example, the contact tracing app of Singapore involves the centralised collection of data from users that test positive, but the app has been met with broad acceptance by its citizens. As citizens of Singapore have a greater trust in their authorities than other countries such as the UK and Germany, they may be less concerned about placing their data in the hands of authorities and more willing to download the app.

Conclusion
As each discipline wrestles for the dominance of their perspective, it is clear that the concept of power is central to contact-tracing apps. While scientists trying to create effective technological solutions are frustrated by the emphasis on data protection rights that hinders their potential, sociologists who are stringent in their privacy standards are unwilling to compromise for technological benefits. These challenges from different disciplines are not necessarily a drawback, and in fact highlight the benefit of taking an interdisciplinary approach to real-world problems. The power struggle prevents one voice from dominating the debate and allows policymakers to achieve an equilibrium in the solution that respects every factor, from the benefits of processing personal data to the necessity of also gaining public trust. There is no one balance that fits every society, as each must reflect their unique laws and sociopolitical contexts, further highlighting the need to see this issue from an interdisciplinary perspective.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
Clarinet/Equipment/Clarinet Models

Having a good instrument is an important part of being a clarinet player. Good instruments help improve your tone and make register jumps easier. A beginner should start with a plastic or resin instrument, and more advanced players should upgrade to a wood instrument.

Brands
There are many well known clarinet brands to choose from. For student instruments, Jupiter, Backun, Yamaha or Selmer instruments are very good starting points. Buffet is well known for creating high-quality professional instruments, but Buffets are far more expensive in general.

Beginner
Jean Paul USA CL-300

Mendini Cecilio MCT

Buffet Prodige

Yamaha YCL-255

Jupiter JCL700N

Backun Alpha

Intermediate
Yamaha YCL-550AL

Buffet E13

Backun Bravo

Professional
Buffet RC

Buffet R13

Yamaha SEVR

title: Clarinet/Equipment/Clarinet Models


text: Issues in Interdisciplinarity 2020-21/Evidence in in the diagnosis of Anxiety


text:
Lentis/Lockheed Martin F-35

Introduction
The Lockheed Martin F-35 is a family of advanced US military fighter jets that are currently in service. The three variants in the F-35 family are the F-35A, F-35B, and F-35C, each variant tailored for specific needs. The A variant is the most typical type of jet, with conventional takeoff and landing (CTOL). The B variant has short takeoff and vertical landing capabilities (STOVL), and is the most expensive of the variants. The C variant was designed specifically for aircraft carriers. Currently, over 595 F-35s are in service worldwide.

Originally, the F-35 was to be a single, highly versatile fighter, having three core capabilities: stealth, vertical takeoff and landing (VTOL), and supersonic flight. The F-35B comes closest to the original expectations, being a supersonic STOVL stealth fighter.

General Capabilities
Stealth was a foundational part of the F-35's core design, governing the shape, internal weapons systems, and embedded components. The F-35 website claims that stealth is the foundation of survivability and lethality.
 * Stealth

The F-35 is equipped with state of the art sensors and radars. It has active electronically scanned array (AESA) radars which can show enemies false targets or attack and jam their networks.
 * Electronic Warfare

The ASQ-239 systems onboard the F-35 provides radar warning, targeting support, ground and airborne threat detection, and self-protection, helping to maintain the advantage in a combat environment. The F-35's automatic ground collision avoidance system uses GPS, terrain data, and spatial awareness to recognize when a jet is headed toward the ground or a mountain.
 * Data Collection, Presentation, and Sharing

The F-35 compiles all gathered information and presents it to the pilot, including the location, distance, direction, and a confidence measure for each determination. The F-35 can also take pictures of its surroundings, allowing an F-35 pilot a 360° view, even peering through the airframe. The F-35 can share threat and image data with nearby allies.

Predecessors
The F-35 family of jets combines capabilities from its predecessors, which vary substantially between each legacy fighter. Each fighter listed below is to be replaced by an appropriate F-35 variant.

 * F-15 Eagle (Air Force)
 * Can perform at high speeds and altitudes (twin engine), 100-0 dogfighting record, operates in all weather conditions, and the main capabilities include air to air and air to surface.
 * The F-15C is mainly meant for air-to-air combat, while the F-15E serves more of a multi-role purpose.

 * A-10 Thunderbolt II "Warthog" (Air Force)
 * Extreme maneuverability at low altitudes, short takeoff and landing, capabilities are mainly focused on close air support and ground attack, and is extremely durable.

 * F-16 Fighting Falcon (Air Force & Navy)
 * Meant to be an upgrade from the F-15 Eagle, cheaper ($14.6 million) and lighter with an emphasis on maintenance and maneuverability. All weather and meant for multi-role, air-to-air and suppression of enemy air defense.

 * F/A-18 Hornet (Marines & Navy)
 * Capabilities include air-to-air and close air support, as well as reconnaissance.

 * F-22 Raptor (Air Force)
 * Offers stealth, revolutionary radar/sensor capabilities. It is highly maneuverable and meant for air-to-air and air-to-ground missions.

Joint Strike Fighter Program
The F-35 was a product of the. Conceived in 1990 by the Department of Defense, this program was created to find a versatile replacement for legacy fighters. To help offset the upfront cost of research and development, the JSF program received funding from many U.S. allies and NATO partner countries.

Part of the JSF program was a competition, held in the late 1990s in which McDonnell Douglas, Northrop Grumman, Lockheed Martin, and Boeing competed for the contract with experimental designs, and later experimental aircraft. Lockheed Martin's X-35 prototype won the contract was slated to become the F-35.

After being awarded the contract, Lockheed Martin partnered with BAE Systems and Northrop Grumman, who are primarily responsible for many of the mission systems including rader, communication devices, and jamming equipment. Lockheed Martin is the prime contractor for the F-35 and is responsible for full system integration, managing the supply chain, and maintenance.

Development and Rollout Timeline

 * 1990s
 * 1996: Lockheed Martin, Boeing, McDonnell Douglas proposed designs for the JSF program.
 * 1996: Boeing and Lockheed Martin were awarded contracts to develop prototypes of their submissions as the X-32 and X-35 respectively.

 * 2000s
 * 2001: The JSF competition concluded and Lockheed Martin was awarded the contract to develop the F-35.
 * 2006: The first F-35 test flight took place.

 * 2010s
 * 2011: Domestic deliveries of all three F-35 variants began.
 * 2014: International debut.

Lockheed Martin anticipates that the F-35 will be in service until 2070.

Application of Economies of Scale
The JSF program sought to take advantage of, the idea that as production increases, the per-unit cost decreases. This phenomenon is possible because there are two costs: a sunk cost and a per-unit cost. Sunk costs can primarily include research and development. Per-unit costs include the parts, labor, maintenance required to manufacture and support each jet. As the number of jets increases, that sunk-cost does not change and is spread out among all jets delivered.

By selecting a single contractor to develop all three variants, and thus maximizing commonality between each variant, the Department of Defense sought to take full advantage of economies of scale with the F-35.

Cost and Schedule Overruns
By the end of the 2000s, the cost of the F-35 program had grown out of control. Development cost and time were double their initial estimates in 2009, and per-plane cost had risen 89% by 2010. In 2011, the program was put on a two-year probation in order to curtail the extravagant spending.

At $89 million per plane and $44,000 per hour in the sky, the costs of the F-35 add up quickly. The program is expected to cost $1 trillion over its 60-year lifespan, approximately $7,000 per taxpayer.

The Failures of "One Size Fits All"
The predicted economies-of-scale benefits of producing three planes under a single program were overshadowed by the complexities this approach introduced. Combining the requirements of the Air Force, the Marines, and the Navy resulted in a plane that was suboptimal for any one branch. Ultimately, the three variants diverged significantly from each other, and a study by RAND estimated that starting from a single base design was more expensive than starting with three separate designs would have been.

The most egregious example of this misjudgment was the weight problem with the F-35B. Lockheed Martin developed this variant for the Marines based on the initial F-35A variant for the Air Force. The Marines, however, required heavier features such as internal weapons bays, and the design exceeded its target weight by 2000 pounds. The necessary redesign to correct the problem set the program back 18 months.

Lessons in Contractor Accountability
The F-35 program suffered from insufficient DoD accountability over Lockheed Martin. Christopher Bogdan, who became the executive officer of the Air Force F-35 program in 2012, sharply criticized the Pentagon's relationship with Lockheed Martin, claiming that "all of the major decisions, whether they be technical, whether they be schedule, whether they be contractual, were really all being made by Lockheed Martin, and the program office was just kind of watching." Bogdan worked to fix this by increasing oversight and working more closely with Lockheed Martin in addressing the F-35's problems.

Former Defense Secretary Ash Carter increased Lockheed Martin's accountability by implementing a cost incentive: for every dollar spent over an agreed-upon target cost, the contractor would bear half the cost, and for every dollar saved under the target, the contractor would keep half of the savings. This approach aligned the incentives of the taxpayer and the contractor, resulting in per-plane costs dropping by two-thirds during the 2010s.

Military-Industrial-Congressional Complex
Former Senator John McCain was a harsh critic of the F-35 program, and had pressured Carter for improvement, saying, "How am I supposed to support the DoD budget the way you want when the voters are reading about this kind of thing?" Dan Grazier, of the Project on Government Oversight, says, "It’s no accident that there are more than 1,500 suppliers for the F-35 program, and they’re spread out to almost every state. That means that there’s basically a veto-proof constituency bloc on Capitol Hill for the F-35 program, so it becomes very difficult for members of Congress to really criticize this program."

F-35 development continued despite steep cost overruns, which may be attributed to an known as the. Representatives in Congress seek to maintain support from F-35 suppliers; the Pentagon seeks to maintain funding from Congress; contractors seek to avoid heavy Pentagon regulation. Lockheed Martin may have set up a wide supplier base to leverage these dependencies, creating a program that was "too big to fail" and securing the company's position in the iron triangle.

For more information, see the Lentis chapter on the military industrial complex.

Potential Chapter Improvements
The Pentagon initially took a hands-off role in the development of the F-35. A deeper investigation into this decision and its current and future ramifications would be a valuable addition to the chapter.

title: Lentis/Lockheed Martin F-35


text:
Lentis/Gentrification

Gentrification is a process of change in a historically disinvested neighborhood. This socio-technical issue arises from the effects of urban renewal efforts. Economic (investment in real estate, city disinvestment) and demographic (higher income residents, education level, racial makeup) changes result from gentrification. Gentrification tends to be most intense in large cities and leads to increased rent, less affordable housing, and disproportionate physical and cultural displacement of minority groups. This chapter will examine historic conditions that contribute to gentrification and look at its impacts in cities across the United States.

Historic Conditions
A variety of historic conditions contribute to neighborhood gentrification:

Redlining White Flight Urban Renewal & Highway System Expansion Central City Investment & Disinvestment Patterns Subprime Lending & The Foreclosure Crisis The culmination of these conditions shows the importance of housing as capital. Gentrification can prevent minority communities from maintaining their homes, and hinder their opportunity to reap the investment benefits of their properties. Housing security is correlated with better health, and less affordable housing negatively impacts the health of a community.
 * The systematic denial of various services by federal agencies, local governments, or the private sector to disadvantaged poor and minority communities. Redlining may include credit, insurance, or loan denial to individuals from neighborhoods classified as risky for investment.
 * Redlining originated from 1930s federal mortgage programs which ranked neighborhoods based on loan worthiness, from least to most risky.
 * The phenomenon of white people moving out of diverse, urban areas and into suburban areas.
 * White flight reduces urban area capital for schools, infrastructure, or other community programs.
 * Funded under the Federal-Aid Highway Act of 1956, highway system expansion aimed to reduce unsafe, packed roadways.
 * Low-income households and communities of color bore the brunt of highway system expansion and urban renewal programs, leading to the displacement of residents, businesses, and neighborhood institutions.
 * As a result, 475,000 households have been displaced across the nation and affected air quality, noise pollution, and real estate value.
 * The phenomenon of people and capital returning to historically disinvested neighborhoods due to housing affordability, the appeal of older homes, proximity to city centers, and improved transit infrastructure.
 * Disproportionate levels of subprime lending in low-income communities of color caused mass foreclosures and left residents vulnerable to investors flipping homes.
 * From 2007-2009, there were 790 foreclosures for African-Americans, 769 foreclosures for Latinos, and 452 for Non-Hispanic Whites per 10,000 loans.

Recent Political Changes
Under the Trump administration, the Tax Cuts and Jobs Act was passed in 2017. The act incentivized investment in state-designated "opportunity zones" -- low-income areas with high unemployment or poor-quality housing. America's wealthiest investors and developers benefited most and built high-end apartment complexes, office towers, and student housing in college towns. Aaron T. Seybert, a social investment officer from the community-development Kresge Foundation in Troy, Michigan said, "perhaps 95% of this is doing no good for people we care about." In 2021, New York state legislators decoupled the state's codes from the federal opportunity zones program. Senator Michael Gianaris (D-Queens) said, "the opportunity zone program is a scandalous giveaway to wealthy developers who didn't need the money to do the development, and I'm glad the state pulled ourselves out of wasting state dollars for this effort."

Community Impacts
Neighborhood change can improve transit options, infrastructure, and community spaces. However, long-time residents seldom enjoy these improvements. As wealthier residents move in, living costs increase and drive out low-income residents. This can lead to the cultural erasure of a neighborhood's previous residents. Those that remain may lose their sense of belonging with their neighbors, customers, and friends moving elsewhere.

The rise of AirBnB contributes to displacement. For example, the AirBnB market in New York City increased rents $380-700 per month in many minority communities. Increased AirBnB postings are positively correlated with gentrification in multiple major cities, such as New York and Los Angeles. AirBnB does not follow local housing regulations, inhibiting affordable housing initiatives. Regulation of short-term rental services like AirBnB in communities most impacted by gentrification can help to mitigate harmful community effects.

Charlottesville, VA
The town of Charlottesville, located within Albemarle County and home to the University of Virginia, experiences community-level gentrification. The affordable housing crisis in the Charlottesville area disproportionately affects communities of color. Since 2012, housing costs have increased 5% each year. From 2000 to 2018, the median income of White residents increased 103%, but increased by only 17% among Black residents. Price inflation and unequal incomes has impacted Black residents more than their White counterparts in search of affordable housing.

Zoning and building patterns tend to fall on racial lines. The Mapping Charlottesville Project found that the Montebello neighborhood off Jefferson-Park Avenue was originally restricted to White residents. In 1965, the City of Charlottesville enforced eminent domain on Vinegar Hill, a predominantly Black neighborhood. This displaced 600+ Black families and 30+ Black-owned businesses. The University of Virginia's Health System is built over Gospel Hill, originally home to many Black families. Many Charlottesville residents feel these changes in cultural diversity. A 2020 survey respondent remarked that Charlottesville "looks more diverse, but in fact families of color are getting pushed out. [It’s a] cloak and dagger process of actually becoming less diverse".

Advocates for low-income and minority residents include the Charlottesville Low-Income Housing Coalition (CLHIC), the Charlottesville Public Housing Association of Residents (PHAR) and Charlottesville Redevelopment and Housing Authority (CRHA), and Cville Plans Together. These organizations provide information on housing, legal assistance, and conduct studies to expose segregationist housing policies in the area, among other strategies.

New York City, NY
A 2016 study conducted by UC Berkeley and NYU's Center for Urban Science and Progress determined over one-third of low-income NYC households were located in neighborhoods at risk of or currently gentrifying. 314 neighborhoods were super-gentrified or exclusive. Chelsea, Harlem, the Lower East Side, Morningside Heights, and Williamsburg were most affected. Researchers at McGill University School of Urban Planning attributed this to higher Airbnb rents. An Airbnb in Lower East Side can cost $6,382 per month, too high for most residents. This rent disparity increases median rent, displacing those unable to pay. During his campaign, Mayor Eric Adams said, "Go back to Iowa. You go back to Ohio. New York City belongs to the people that were here and made New York City what it is." He later claimed this was a gaffe. "On the campaign trail, you experience political gaffes. And the sentence was a gaffe." On Twitter, Mayor Adams commented "New York City is always changing, but every once in a while, there's a sea change -- that's what's happening right now as neighborhoods lose Black residents due to rapid gentrification."

The Brooklyn Anti-Gentrification Network (BAN) advocates for universally stabilized rent, the end of tenant harassment, and end of income-segregated housing developments. BAN wants "Not one more person displaced! Not one more luxury development, until we have affordable housing for all!” The Association for Neighborhood & Housing Development (ANHD) is another advocacy building "community power in order to create better laws and policies, protect tenants’ rights, and strengthen neighborhoods." Activists also work to resist gentrification and displacement through art, marches, and petitions. For example, Mi Casa No Es Su Casa is a political art project installing signage across NYC. The Brooklyn Anti-Gentrification Network, Movement to Protect the People, and Equality for Flatbush collaborated to lead a March Against Gentrification, Racism, and Police Violence. Organizations such as the Urban Displacement Project are working to map gentrification and displacement to raise awareness.

San Francisco, CA
The Mission District of San Francisco, a Latino enclave just outside of Silicon Valley, is known as the “poster child of gentrification”. . The Dot-com bubble period from 1995 to 2004 greatly contributed to gentrification. An influx of high-income tech employees moved from Silicon Valley to the lower-income Mission District. In response to the influx, one long-time resident stated, “this block will never be the same.” The Latino population fell 6% while the White population grew 7%. Family households in the district fell 19% from 1980 to 2013. During this time, more residents held a bachelor's degree, and median income increased 84%.

The tech influx led to commercial and residential displacement. From 2000 to 2013, the vacancy rate rose 4.6%. The increasing costs of a one bedroom apartment made it difficult for lower-income residents to stay. Evicted residents also struggled to find affordable housing due to vacancy decontrol, a landlord's ability to raise rent when tenants move out. With many higher-end retail stores in the area and more vacancy decontrol, landlords appealed to the wealthier tenants. Tenant buyouts allowed landlords to give cash to outgoing tenants to make room for new wealthier tenants.

A proposed ten-story luxury apartment complex in the district highlights the social and cultural complexity of gentrification. The complex, called Plaza 16, would remove a Walgreens, Burger King, Chinese restaurants, and other businesses employing local residents. The local Latino population felt neglected, as their lives would be significantly impacted to make room for richer tech employees. The luxury condos were also predicted to impact traffic, schools and housing prices in the area.

The Mission Economic Development Agency (MEDA) advocates against gentrification and empowers residents in the district. MEDA works to advance "a national equity movement by building Latino prosperity, community ownership, and civic power," and leads the "right-to-purchase" movement, an effort to help tenants purchase property and avoid displacement. With MEDA's help, low-income Black and Latino residents have been able to buy their buildings and revive the community.

Washington, D.C.
Since 2000, Washington, D.C. has experienced the most gentrification and displacement. Despite the city's Black population falling by 23%, the overall population grew 19% due to a 202% increase in White residents. The Shaw neighborhood in D.C. exemplifies gentrification in the city. Between 1980 to 2010, the Black population in Shaw decreased 34%. Additionally, median home values have increased nearly 430%. During this time, new and old residents clashed, as many believed the neighborhood was changing for the worse. This led to older, lower-income residents experiencing cultural and political alienation as community infrastructure improvements continued.

Virginia Lee, a longtime resident of Shaw, said that, “all the goodness that has come with the gentrification…all that has come has been material in nature and very little has been done to preserve the human aspect of a city that’s being transformed.” Older residents are fighting for access to affordable housing while struggling to preserve the cultural and historical heritage of their neighborhoods. Newcomers to the community - often of a different race, educational background, and socioeconomic status- have been called the creative class. Some activists argue that the city is purposely crafting zoning laws and policies trying to attract the creative class to increase tax revenue and become more prosperous. Housing developers that seek to buy low-price properties, renovate them, and sell them for profit have also contributed to gentrification in the city.

Advocacies like ONE DC and Justice First work to fight against gentrification in Shaw and the district. Founded in 1997, ONE DC strives to "preserve racial and economic equity" in Shaw. Efforts have been made to resist displacement; ONE DC works to organize with tenants to "convert tenant buildings in cooperatives." Justice First advocates for housing as a right, offering resources for displaced tenants and campaigning for rent control.

Conclusion
The examples of gentrification in the United States in this chapter suggest solutions must be comprehensive and intersectional. Remedial policies must consider historic and systemic disinvestment in minority communities. Investments in historically disinvested communities requires investments in the residents themselves. Community empowerment, enhancing tenant's rights, expanding affordable housing, and offering small business loans can help prevent displacement from gentrification and improve existing residents' lives.

Suggested solutions to gentrification require more investigation into their efficacy. It is also necessary to further define the true effects gentrification has on communities. As with any socio-technical issue, the intersection of technology and society can work to develop a comprehensive solution to negative gentrification impacts.

title: Lentis/Gentrification


text: Issues in Interdisciplinarity 2020-21/Evidence in Effectiveness of Lockdown


text:
Lentis/Social Media Shaming Campaigns

Overview
is a type of public shaming with the goal of humiliating a person, group of people, or corporation. Public shaming has been a feature of societies throughout much of human history, and has now adapted to the modern age. It can be done over many mediums as long as it is done through the internet, this means it can use internet forums (e.g. Reddit or 4chan), or it can use local mediums (e.g. Texting or emailing). There are multiple methods that are currently seen, some of these include canceling, doxing, review bombing, and social status shaming. Controversies are arising over these campaigns looking into the loss of human rights that will inherently happen when a person is humiliated publicly. With these growing controversies and the lack of regulation on some of these forums there are questions of what the future holds for online shaming.

History
Shaming as a method of social change is prevalent throughout history due to its effectiveness. Historical examples of shaming include pillories, wearing unfavorable extensions (e.g. the shame flute, the letter A from The Scarlet Letter, etc.), and even forms of corporal punishment. Research has shown that even non-physical forms of shaming can have lifetime consequences to someone; including PTSD, paranoia, and anxiety. Physical pain and public shaming have been proven to have almost identical psychological effects. Left unattended, shaming campaigns against individuals or social groups can result in projections onto other groups or individuals, causing a collateral and cascading effect. This concept can be used to explain the rise of the Nazi party post World War I, as well as Confederate groups throughout southern USA post civil war.

In the advent of media, methods of communication expanded beyond in-person experience. People could become more easily and permanently influenced by credible individuals and social institutions of the time. Anonymity allowed both institutions and individuals to exhibit greater social control that could otherwise be shamed. One famous example of the intersection between anonymity and shaming is the beginning of the downfall of the KKK caused partially by the Superman radio show in the late 1940s, which revealed their secrets. Simply, individuals and institutions tend to be more careful with their words and actions when under the public eye. As the age of information dawned, the ability to hide one's identity became just as easy as openly sharing their opinion.

Social constructs
Shame is distinct from guilt. Shame is based around perception of how one is seen by others, even if the one feeling shame does not consider the action morally wrong. Guilt is an internal remorse at having done something one considers morally wrong, even if others are largely unaware of it. Shaming campaigns are similarly intended to: 1) influence public perception of the shamed, and 2) change the behavior of the shamed and, in this regard, are campaigns intended to invoke shame or guilt in the shamed.

Shame and guilt are generally only felt when three factors apply: 1) self-identity with a group 2) self-perceived transgression of in-group norms. 3) perception of these norms as valid. Women are quicker to feel shame than men, while adolescents feel shame more intensely than adults. In general, some people are more “shame-prone” while others are more “guilt-prone.” Severe shame and guilt can both be associated with depression and other mental illnesses. These factors can help explain why similar shaming campaigns may affect some individuals more intensely than others.

Several factors can influence how likely a subset of users of a social media website are to engage in a shaming campaign. These can include echo chambers, anonymity, and polarization, among others. These can contribute to spread and acceptance of biased or false information, from which shaming can occur.

A Pew Research survey found that 83% of respondents ignore political content posted by their friends that they disagree with, while 59% say that discussing politics on social media with someone that they disagree with is “stressful and frustrating” rather than “interesting and informative.” 39% of respondents say that they have modified settings to filter out posts from a specific user on their feed because of politics, with 60% of these saying that the reason is due to offensive political content. These statistics potentially indicate that users can experience an echo chamber effect through their own choices, even without the presence of algorithmic influence.

Algorithmic influence can cause filter bubbles. Eli Pariser, the activist and researcher who coined the term, argues that the closed-source nature of social media algorithms obscures how websites choose which content to display to users. The Filter Bubble Transparency Act is a proposed bipartisan bill to create algorithmic transparency.

Modern Examples
Modern examples can fall into at at least two cases: Internet vigilantism and political/moral shaming.

Internet vigilantism can be further subdivided into two cases: 1) reminding the Internet of an incident widely believed to be negative and of who caused the incident, and 2) attempting to identify a perpetrator of a negative event who is not yet identified by authorities.

Some examples of case 1 include but are not limited to:

 * Brock Turner, the Stanford swimmer who raped a woman, was shamed after serving only three months in jail as a result. Internet publicization of the crime and incident has contributed to his inability to get a job above entry level. It has also contributed to his becoming the “textbook definition” of a rapist.

 * Martin Shkreli, a hedge fund manager, was shamed for several reasons including raising the price of Daraprim from $13.50 to over $750 per pill and making disparaging comments towards female reporters. His wealth has largely isolated him from consequences for these behaviors, although he has been suspended from Twitter. He has gone to jail for reasons unrelated to the shaming.

Some examples of case 2 include, but are not limited to:

 * Sunil Tripathi, a college student, was shamed after being misidentified by Reddit as the Boston Bomber. His family publicized pictures of him after his disappearance (later found to be suicide). In response, they were harassed for seemingly protecting a terrorist.

 * Peter Weinberg, a finance marketing executive, was doxed after being accused of being the Bethesda biker. At the time of the accusations, local police had already cleared him as a suspect.

Implications of Shaming Campaigns
Remnants of shame culture have stayed prevalent in societies either as a form of honor or fear. In western cultures, especially with more conservative ideologies, shaming is used as a familial builder. Shaming members of an in-group can create a stronger sense of community among members, and help an individual become less vulnerable to shaming from out-groups. Online shaming campaigns; however, exhibit the use of shame as a method of accusation, suppression, or aggression.

Ethics on minority perception are usually brought up by coexisting cultures. Online shaming has pros and cons that make it a nuanced subject. Although it is possible for shaming to cause a desired social outcome, such as comradery in western cultures, vilifying others does more harm than good. There are arguments for solving power inequalities with these campaigns. However, these arguments usually involve shaming an ideology, not a specific person. Shaming campaigns mentioned above that resulted in wrongful accusation and even suicide always involved shaming a specific person. Shaming is a powerful social tool that is included in the fundamental human right to speech, and can be effective at suppressing unethical actions and comments alike. Social institutions typically known for their overall good (e.g. universities, governments) have been successfully shamed to create social change without giving rise to anti shaming by criticizing specific actions rather than the institution itself. Giving a voice to everyone allows oppression itself to be shamed. Others argue that without being in person, we are not guiding people to a new standard.

Future
The future of these campaigns will be determined by future regulations. The U.N. Human Rights Campaign is already starting to look into regulations, releasing this statement “No one shall be subjected to arbitrary or unlawful interference with his or her privacy, family, home or correspondence, nor to unlawful attacks on his or her honour and reputation.” With this there is belief that more governments will start looking into solutions to keep people safer from shaming campaigns. Some new systems that might be implemented include keeping a person's identity more private on these forums. This will allow for the people being shamed to not lose their entire life over the course of one of these events. Other schools of thought examines these campaigns and hopes to implement systems that rehabilitate as opposed to just shaming. The belief is that there is no benefit of fully excluding a person from society, and that these campaigns can be used to better society. Without data on these new systems it is unclear if either of these systems, or any other systems, could work when fully implemented.

Conclusions
Online shaming is an extension of numerous public humiliations that have been a part of societies over the ages. There are numerous philosophies that come from different societies in how shaming can be used within their population, and has varying affects on different cultures and subgroups within them. These campaigns are becoming more prevalent with more cases arising through the adoption of technology. Prominent figures such as politicians, and celebrities are being brought into these campaigns. As campaigns become bigger there is a call to find ways of keeping people safe, and implementing new systems in current online mediums.

title: Lentis/Social Media Shaming Campaigns


text:
User talk:TheColdPrince

Welcome!
Welcome, TheColdPrince!

If you have any questions, you can ask in the assistance reading room or possibly contact me personally.

Hi. I've deleted your recent page as it doesn't fit the scope of Wikibooks. Please read WB:What is Wikibooks?. —Atcovi (Talk - Contribs) 22:50, 5 December 2020 (UTC)

title: User talk:TheColdPrince


text:
Lentis/Among Us: Social Behavior in a Virtual World

Among Us is an online multiplayer social deduction game developed by InnerSloth and released on June 15, 2018; up to ten players are stuck on a spaceship with 1-3 imposters. Players must vote out all the imposters or complete all their tasks to win the game. In each round, there are two phases. During the task phase, players try to finish their jobs and avoid getting killed by the imposters. All forms of communication are disabled in the task phase. The discussion phase is started upon discovering a body or pushing the emergency meeting button. Players debate the identity of the killer using the text chat and vote on a suspect. Suspects are ejected from the ship if a plurality is reached.

Among Us as a Sociotechnical Case
Among Us participants include public lobby players, private lobby players, hackers, and streamers. Among Us provides insight into player psychology and social behavior in an unmoderated environment.

Rise in Popularity
Among Us downloads increased by 20 times in 2020. Well-known streamers, such as sodapoppin and xQc, began playing the game and collaborating with other popular streamers. Due to COVID-19, many countries and states have implemented "stay-at-home" orders and other restrictions. People cannot gather or meet with friends. People are social animals; social relationships affect mental health, physical health, and mortality risk. Users can interact remotely with friends through Among Us.

Participation
Among Us built its popularity upon the success of existing games. In other social deduction games such as Mafia, the majority of players can only participate during discussions. Once players are dead, they can't participate. In Among Us, everyone has tasks, even after death, so the players stay engaged.

Consequences
Due to the game’s popularity, many toxic players flooded the public lobbies in Among Us. These players spammed chats with negative comments and targeted other players. Eris Loris, a hacker, flooded chats with pro-Trump messages. InnerSloth had to make an emergency update to address this issue and urged players to use private lobbies.

Many politicians have started using Twitch as a platform and utilized Among Us as a political tool. Most notably, Alexandria Ocasio-Cortez started streaming Among Us on Twitch in an attempt to mobilize young people to vote in the 2020 election. She was able to gather more than 400,000 viewers to watch her stream. However, others criticized her for “electioneering”

Accountability
Accountability is an "obligation or willingness to accept responsibility or to account for one’s action". In video games, users demonstrating poor behavior such as inappropriate language, harassment, or cheating can be banned or suspended.

Anonymous Players
Users are anonymous; players are not associated with accounts and nicknames can be changed freely. InnerSloth gives players who quit three games in a row a five-minute penalty. Because people are not held accountable, this encourages selfish behavior; many people leave games. Users have started a petition on Reddit to set a ten-minute penalty for players that leave at the start of a match; as of December 7, 2020, this petition has received 14,200 upvotes.

Lack of Engagement
Users are not engaged with each individual session because they can easily join other games. Players feel they are at an unfair disadvantage because they expect to have more players on their side. Some users in a lobby may form groups so they communicate with third-party software or speak a different language.

During its surge in popularity, Filipino users flooded the English chat due to not having a dedicated server, and many exhibited toxic behaviors such as voting off English-speaking players. Many players became angry because they couldn't engage in conversations. Froyobowl expressed this sentiment in a Reddit post which received 2,500 upvotes as of December 7, 2020, that stated "Please stop ruining the game for others;" "English chat is not a Filipino Tagalog". InnerSloth has since added a Filipino chat, but users are tired of the lack of accountability. Steam Among Us player count has dropped from 176,453 average players in October 2020 to 114,334 in November, 2020.

Trust
Trust is the ability to depend on another; a trustworthy person is perceived as benevolent (kind), ethical, competent (can complete tasks), and predictable. Among Us fosters distrust between users. Players explore a large map with limited vision. Walls block their sight, and people need to perform tasks in different locations. Users have trouble communicating and tracking each other without a chatbox. An imposter is disguised among the crew. Reciprocal norm is the social convention that compels people to return a favor. Imposters can utilize this behavior to gain trust from crewmates; by defending another player, that player will then feel compelled to defend the imposter.

Gathering Information
Users gather information by checking player locations at the Command Center, viewing the Security Room cameras, walking around, etc. Visuals tasks such as medbay scan can be seen by other players and guarantee a crewmate's innocence. Players can group together to watch each other and provide alibis. Users may change their behavior depending on the situation. For example, if a group of friends is in a lobby, they may feel safe grouping because they trust one another. A user with a medbay scan task may choose to call an emergency meeting so that he can tell other players to follow him and confirm his innocence. Players gather information directly with their senses or indirectly through conversations with other players. However, indirect information is not always accurate.

Eyewitness Testimony
In 1974, Loftus and Palmer studied if the language in eyewitness testimony can alter memory. They asked test participants to estimate motor vehicle speed from a crash video. Participants were asked with different verbs "About how fast were the cars going when they (smashed/collided/bumped/hit/contacted) each other?" Stronger verbs (smashed) correlated with higher speed estimations. The experiment showed that leading questions modified the witness's perception and memory of the event.

In Among Us, imposters can only kill adjacent crewmates. During the voting session, players discuss the location of the body and vouch for each other. Players are generally referred to by their character's unique color. For example, if Blue was killed on the West end of the ship and Red was witnessed at the East end immediately prior to the murder, then they cannot be the imposter due to their location alibi. Likewise, if Red was seen running away from the room where the body was found, then they are considered suspicious. The discussion revolves around the eyewitness testimonies of the players.

Imposters need to explain their own logical inconsistencies and lack of alibis to the rest of the group in order to avoid suspicion and getting caught. Imposters want to take control and change the narrative through the use of leading questions and statements to confuse crewmates and deflect accusations.

Groupthink
A mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members' strivings for unanimity override their motivation to realistically appraise alternative courses of action.

Once an accusation is made, especially in public lobbies lacking voice chat, other players often immediately vote to eject the accused. This can occur before the accused is given a chance to defend themselves. As a team, the crewmates want to make logical decisions and root out the imposter with minimal casualties. However, as an individual, it is best to go along with the collective decision. Dissenters are deemed suspicious in later rounds. The desire for group voting unanimity leads to irrational decision-making and avoiding alternative solutions such as abstaining until further information is gathered.

Bystander Effect
In experiments prompted by the brutal murder of Kitty Genovese, social psychologists Latané and Darley attributed the bystander effect to two factors: diffusion of responsibility and social influence. The perceived diffusion of responsibility means that individuals feel less personal responsibility to take action when there are more onlookers. Social influence means that individuals monitor the behavior of those around them to determine how to act. The lack of action can have tragic consequences such as the one-hour long drowning of Raymond Zach 150 yards from the shore despite the presence of firefighters, police officers, and roughly 75 beachgoers.

Imposters can sabotage critical components which starts a countdown timer. Crewmates lose if the timer ends before the component is fixed. Fixing the component takes time away from a crewmate's task. With so many other players, users often assume that someone else is fixing it.

Conclusion
Further work is needed to extend the examination to specific aspects of game theory, the study of calculated interactions among rational agents. In Among Us, players are motivated to win. Each player decides whom to trust and whom to vote against based on imperfect information and their own logical conclusions. Other video games can also be examined for their insights into human behavior. Video games serve as the perfect environment for studying game theory due to clear definitions of winning and solidified rules. Among Us' issue of accountability is explored in another Lentis casebook section regarding the hidden identities of players.


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Lentis/Miracle Rice

Introduction
IR8 is a High Yield Variety (HYV) rice species developed in 1966 to support the growing populations of southeast Asia. Its tenfold yield compared to traditional rice strains prevented the starvation of millions, earning it the moniker "miracle rice". While miracle rice's benefits are substantial, its growth requires more fertilizers, pesticides, and water than other rices. This chapter discusses miracle rice's history, the sociotechnical benefits and consequences of its usage, and its reception compared to other novel rice species.

Expanding Global Population and the Green Revolution
Following the second world war, famine was a primary global concern. As populations worldwide began to grow exponentially, traditional farming methodologies became increasingly insufficient and new agricultural practices were necessary. Countries around the world looked towards the global superpowers of the United States and USSR for aid in combating hunger. In addition to humanitarian concerns, U.S. officials wanted to prevent the expansion of communism into impoverished nations, and so the U.S. introduced technological agricultural solutions to these countries as a countermeasure against USSR influence. The resulting adoption of irrigation systems, fertilizers, pesticides, and HYV crops by these developing countries was called the "Green Revolution" by William S. Gaud in his address to congress on March 8, 1968:

"'These and other developments in the field of agriculture contain the makings of a new revolution. It is not a violent Red Revolution like that of the Soviets, nor is it a White Revolution like that of the Shah of Iran. I call it the Green Revolution.'"

While initial efforts of the Green Revolution began in Mexico in the 1950s, the 1.4 billion people of Asia quickly demanded attention. A staple crop for many Asian countries, rice was ideal for HYV development. In 1960, the Ford and Rockefeller Foundations founded the International Rice Research Institute (IRRI) with the government of the Republic of the Philippines to crossbreed new HYV rice strains.

Development of IR8
IRRI scientists in charge of crossbreeding HYV rices understood the factor limiting rice yields was the height of rice stems. Many rice species grew too tall with flimsy stalks, making them susceptible to falling over (or "lodging"). After lodging, rice crops ceased to grow and either decomposed or were consumed by crop pests. The IRRI therefore aimed to crossbreed rice strains with short, stout stems with tall, higher yield species to maximize production and prevent lodging. Peta rice, a tall Indonesian rice, and Dee-geo-woo-gen (DGWG), a short Chinese rice, were among the first pairs to produce promising crossbreeding results in 1964. After selective crossbreeding five generations of the initial Peta and DGWG strains, IR8 was produced in 1966 with outstanding results. While typical crop crossbreeding efforts increase yields 1-2 percent per year, IR8 increased yields tenfold after just two years of development. IR8 was immediately adopted in the Philippines and shortly earned its title of "miracle rice" by the Pilipino press. In the following years, miracle rice became the staple crop of many southeast Asian countries including India, Thailand, and Vietnam.

Successes of IR8
IR8 was successful in solving the famine problem in Asia. Prior to the Green Revolution, 50% of the population in Asia was hungry, now it's 12%. The tenfold yield was never before seen in any form of rice. The hybrid also grew shorter than traditional rice. Traditional rice had the problem of growing too tall and falling over. Much more of the sun's energy went into making the IR8 grain. This allowed for more grain per plant.

Shortcomings of IR8
While IR8 was tremendously successful, many rice farmers eventually moved away from it. First, the rice was not particularly tasty, often described as being chalky and hardened after cooking. The rice was not able to handle unfavorable farming land as well as traditional rice, decreasing its yield. Asia is only around 50% ideal farming land, while the rest consists of wetter, more swampy, and/or flooding conditions. IR8 was also harsh on the fertilizer. Farmers would have to invest a lot in chemicals to keep the fertilizer farmable. Various other chemicals such as pesticides were also needed due to many weeds growing and diseases forming. Overall This operation became financially questionable, especially as the cost of growing traditional rice became much cheaper.

Comparison with Golden Rice
Miracle rice was largely met with positive reception, and was adopted by farmers worldwide. Despite there being negative environmental effects and potential sustainability risks, environmental activist groups did not oppose the use of miracle rice, nor was there widespread concern about the method of its development. This contrasts sharply with "Golden Rice," a novel rice variety which is genetically engineered to be more nutritious. Despite the fact both varieties were developed by the IRRI and both varieties' genetics were manipulated to have more desirable traits, the difference in response has been large. Unlike with miracle rice, activist groups such as Greenpeace strongly oppose the use of golden rice, stating that its use is "environmentally irresponsible, poses risks to human health and could compromise food, nutrition and financial security." Public concern has primarily been focused on the method of golden rice's development, namely intentional genetic editing, and this concern poses a roadblock to more ambitious developments in rice. A 2016 poll from Pew Research indicate that 49% of adults in the U.S. believe that genetically modified foods are likely to have negative effects on the environment, and 49% of adults believe GM foods are likely to have negative health impacts on the population. The fact that miracle rice was produced through crossbreeding, a technique that has been used for millennia, instead of genetic engineering, which is relatively novel, may account for the discrepancy in opinions on the two varieties.

Conclusions
Miracle rice is a prime example of how purely technological solutions can provide substantial short term benefits at the expense of creating long term obstacles. While miracle rice saved millions of lives, its farmers became reliant on fertilizers and pesticides, causing severe environmental and economic consequences. Furthermore, Asian populations were not merely maintained; they continued to grow exponentially after miracle rice's introduction. Today, Indian officials have stated that overpopulation in the country is one of its most major concerns, and rising unemployment, overcrowded infrastructures, and drained natural resources are all direct repercussions. By pursuing solely technological avenues to fix hunger worldwide, miracle rice and the Green Revolution ignored any potential logistical solutions which may have avoided fertilizer and pesticide dependencies or the consequences of overpopulation. While further analysis is required to fully understand why miracle rice was so readily received compared to other novel crops, it is clear that single-minded technological solutions can have potentially devastating consequences.

title: Lentis/Miracle Rice


text:
LMIs in Control/Applications/Hinf LMI SatelliteAttitudeControl

This is a $$H_{\infty}$$ LMI for Satellite Attitude Control. Satellite attitude control is necessary to allow satellites in orbit accomplish their mission. Poor satellite attitude control results in poor pointing performance which can result in increased cost, delayed service, and reduced lifetime of the satellite.

The System
The full derivation of the system from first principles is accomplished in the companion LMI for $$H_2$$ Satellite Attitude Control. The link to that page is at the bottom with the references.

Continuous Time:

\begin{align} I_x\ddot{\phi}+4(I_y-I_z)\omega _0^2 \phi + (I_y - I_z - I_z)\omega _0 \dot{\psi} &= T_{cx} + T_{dx}\\ I_y\ddot{\theta}+3(I_x-I_z)\omega _0^2 \theta &= T_{cy} + T_{dy}\\ I_z\ddot{\psi}+(I_x+I_z-I_y)\omega _0 \dot{\psi} &= T_{cz} + T_{dz}\\ \end{align}$$

The above model was derived by substituting satellite attitude kinematics into the attitude dynamics of a satellite. The following are definitions of the variables above:

 * Moments of inertia about the corresponding axis: $$I_x, I_y, I_z $$
 * Euler Angles: $$\phi,\theta,\psi $$
 * Disturbance Torques (flywheel, gravitational, and disturbance): $$T_c, T_g, T_d $$
 * Rotational-angular velocity of the Earth: $$ \omega _0 = 7.292115x10^{-5} \text{ [rad/s]} $$

The state-space representation of the system can be found by the following steps. Let

\begin{align} x &= \begin{bmatrix} q & \dot{q} \end{bmatrix}^T,& z_{\infty} = 10^{-3}M\ddot{q},& z_2 = q,\\ \end{align}$$

Introduce the notations

\begin{align} I_{\alpha \beta}&=I_{\alpha}-I_{\beta}, & I_{\alpha \beta \gamma} = I_{\alpha} - I_{\beta} - I_{\gamma}\\ \end{align}$$

where $$ \alpha, \beta, \gamma $$ stand for any element in $${x,y,z}$$. Then the state-space system is:

\begin{align} \dot{x} &= Ax + B_1u+B_2d \\ z_{\infty} &= C_1x+D_1u+D_2d \\ z_2 &= C_2x \\ \end{align}$$

where the matrices in the above state-space representation are defined as follows:

\begin{align} A = \begin{bmatrix} 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 \\               0 & 0 & 0 & 0 & 0 & 1 \\                \frac{-4\omega_0^2 I_{yz}}{I_x}  & 0 & 0 & 0 & 0 & \frac{-\omega_0 I_{yzx}}{I_x} \\ 0 & \frac{-3\omega_0^2 I_{xz}}{I_y} & 0 & 0 & 0 & 0 \\ 0 & 0 & \frac{-\omega_0^2 I_{yx}}{I_z} & \frac{\omega_0 I_{yzx}}{I_x} & 0 & 0 \end{bmatrix}\\ \\ B_1 = B_2 = \begin{bmatrix} 0 & 0 & 0 \\ 0 & 0 & 0 \\                           0 & 0 & 0 \\                            \frac{1}{I_x} & 0 & 0 \\ 0 & \frac{1}{I_y} & 0 \\ 0 & 0 & \frac{1}{I_z} \\ \end{bmatrix}\\ \\ C_1 = 10^{-3} \times \begin{bmatrix} -4\omega_0^2I_{yz} & 0 & 0 & 0 & -\omega_0 I_{yxz}\\ 0 & -3\omega_0^2 I_{xz} & 0 & 0 & 0 & 0\\ 0 & 0 & -\omega_0^2 I_{yx} & \omega_0 I_{yxz} & 0 & 0 \end{bmatrix}\\ \\ C_2 = \begin{bmatrix} I_{3x3} & 0_{3x3} \end{bmatrix} \\ \\ D_1 = 10^{-3} \times L_1, D_2 = 10^{-3} \times L_2 \end{align}$$

The Data
Data required for this LMI include moments of inertia of the satellite being controlled and the angular velocity of the earth. Any knowledge of the disturbance torques would also facilitate solution of the problem.

The Optimization Problem
The idea is to design a state feedback control law for the previous satellite state-space system of the form

u = Kx $$

This control law is designed so that the closed-loop system is stable and the transfer function matrix from disturbance to output

G_{z_\infty d}(s) = (C_1+D_1K)(sI-(A+B_1K))^{-1}B_2+D_2 $$

satisfies

$$
 * G_{z_\infty d}(s)||_{\infty} = (C_1+D_1K)(sI-(A+B_1K))^{-1}B_2+D_2

for a minimal positive scalar $$\gamma_{\infty}$$ which represents the minimum attenuation level.

The idea here is to attenuate the disturbances as much as possible while still maintaining the ability of the satellite to track. This minimum attenuation level is found from the LMI in the following section.

 * Objective: Hinf norm
 * Variables: Controller Gains
 * Constraints: Satellite Attitude Dynamics and Kinematics. Maximum safe rotational rate of Satellite, maximum jet pulse thrust

The LMI: $$H_{\infty}$$ Feedback Control of the Satellite System
Duan and Yu approach the $$H_{\infty}$$ satellite system as follows. The minimum attenuation level from disturbance to output can be found by solving the following LMI optimization problem.

\begin{align} \\ &min \gamma_{\infty} \\ \\ &\text{s.t. } X > 0 \\ \\ &\begin{bmatrix} (AX+B_1W)^T+AX+B_1W & B_2 & (C_1X+D_1W)^T \\ B_2^T & -\gamma_{H\infty} I & D_2^T \\ C_1X+D_1W & D_2 & -\gamma_{H\infty} I \end{bmatrix} < 0 \\

\end{align}$$

which is the same as Theorem 8.1 in Duan and Yu's Book, the solution to the $$ H_{\infty} $$ problem.

Conclusion:
The Duan and Yu textbook takes as typical values of the satellite moment of inertias as:

\begin{align}

I_x = 1030.17 kg\cdot m^2, I_y = 3015.65kg\cdot m^2, I_Z = 3030.43kg\cdot m^2\\

\end{align}$$

They then proceed to solve the optimization problem to find a controller gain that yields an attenuation level of 0.0010. Though this value is very small and represents very good attenuation the optimized controller pushes the poles of the closed loop system very close to the imaginary axis, resulting in slow oscillatory behavior with a very long settling time.

To address this a second approach was used by the authors which involves modifying the final LMI in the expression above and requiring that it be constrained as follows

\begin{align} &-I < \begin{bmatrix} (AX+B_1W)^T+AX+B_1W & B_2 & (C_1X+D_1W)^T \\ B_2^T & -\gamma_{H\infty} I & D_2^T \\ C_1X+D_1W & D_2 & -\gamma_{H\infty} I \end{bmatrix} < 0 \\ \end{align}$$

These results are planned verified in the linked code implementation using YALMIP, whereas the authors took advantage of the MATLAB LMI Toolbox to achieve their results.

Implementation
A link to CodeOcean or other online implementation of the LMI

Related LMIs
Links to other closely-related LMIs

 * H2_LMI_SatelliteAttitudeControl
 * Optimal_Output_Feedback_Hinf_LMI
 * Full-State_Feedback_Optimal_Control_Hinf_LMI


text:
LMIs in Control/Applications/H2 LMI SatelliteAttitudeControl

This is a H2 LMI for Satellite Attitude Control

Satellite attitude control is important for military, civil, and scientific activities. Attitude control of a satellite involves fast maneuvering and accurate pointing in the presence of all kinds of disturbances and parameter uncertainties.

The System
The satellite state-space formulation is given in the $$H_{\infty}$$ LMI page for Satellite Attitude Control which is also in the applications section of this WikiBook. This section discusses the derivation of that state-space formulation based on first principles.

The attitude dynamics of a satellite in an inertial coordinate system can be described in terms of the time rate of change of its angular momentum and the sum of the external torques and moments acting on the system. That is:

\begin{align} \dot H&=T_c+T_g+T_d,\\ H&=I_b\omega\\ \end{align}$$

where the following variables are defined as follows:

 * $$T_c,T_g, \text{ and } T_d $$ are the flywheel torque, the gravitational torque, and the disturbance torque.
 * $$H$$ is the total momentum/torque acting on the satellite
 * $$I_b$$ is the inertia matrix/tensor for the satellite
 * $$\omega$$ is the angular velocity vector of the satellite.

The time derivative of the total angular momentum in an arbitrary rotating reference frame (such as the body frame of the satellite) is given by:

\begin{align} \dot H&=I_b\dot{\omega}+\omega \times (I_b \omega),\\ \end{align}$$

which takes into the account of the angular velocity of the rotating reference frame relative to the inertial reference frame where Newton's laws are valid.

Combining equations, collecting terms and choosing the principle axes of the spacecraft so that the Inertia Tensor is diagonalized yields the following equations of motion:

\begin{align} I_b \dot{\omega} + \omega \times (I_b \omega) = T_c + T_g + T_d,\\ \end{align}$$

Using the small angle approximation, the angular velocity of the satellite in the inertial coordinate system represented in the body coordinate system can be written as

\begin{align} \omega = \begin{bmatrix} \omega _x \\ \omega _y \\ \omega_z \end{bmatrix} = \begin{bmatrix} \dot{\phi} - \omega _0 \psi \\ \dot{\theta} - \omega _0 \\ \dot{\psi} + \omega _0 \psi \end{bmatrix} \\ \text{where } \omega _0 = 7.292115 \times 10^{-5} \text{ [rad/s]} \\ \end{align}$$

These equations form the basis of the state-space representation used in the H-inf LMI for satellite attitude control. For clarity, they are repeated below.

\begin{align} \dot{x} &= Ax + B_1u+B_2d \\ z_{\infty} &= C_1x+D_1u+D_2d \\ z_2 &= C_2x \\ \\ \end{align}$$

\begin{align} A = \begin{bmatrix} 0 & 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 \\               0 & 0 & 0 & 0 & 0 & 1 \\                \frac{-4\omega_0^2 I_{yz}}{I_x}  & 0 & 0 & 0 & 0 & \frac{-\omega_0 I_{yzx}}{I_x} \\ 0 & \frac{-3\omega_0^2 I_{xz}}{I_y} & 0 & 0 & 0 & 0 \\ 0 & 0 & \frac{-\omega_0^2 I_{yx}}{I_z} & \frac{\omega_0 I_{yzx}}{I_x} & 0 & 0 \end{bmatrix}\\ \\ B_1 = B_2 = \begin{bmatrix} 0 & 0 & 0 \\ 0 & 0 & 0 \\                           0 & 0 & 0 \\                            \frac{1}{I_x} & 0 & 0 \\ 0 & \frac{1}{I_y} & 0 \\ 0 & 0 & \frac{1}{I_z} \\ \end{bmatrix}\\ \\ C_1 = 10^{-3} \times \begin{bmatrix} -4\omega_0^2I_{yz} & 0 & 0 & 0 & -\omega_0 I_{yxz}\\ 0 & -3\omega_0^2 I_{xz} & 0 & 0 & 0 & 0\\ 0 & 0 & -\omega_0^2 I_{yx} & \omega_0 I_{yxz} & 0 & 0 \end{bmatrix}\\ \\ C_2 = \begin{bmatrix} I_{3x3} & 0_{3x3} \end{bmatrix} \\ \\ D_1 = 10^{-3} \times L_1, D_2 = 10^{-3} \times L_2 \end{align}$$

The Data
Data required for this LMI include moments of inertia of the satellite being controlled and the angular velocity of the earth. Any knowledge of the disturbance torques would also facilitate solution of the problem.

The Optimization Problem
The optimization problem seeks to minimize the H2 norm of the transfer function from disturbance to output. Thus, we expect slightly different results than the H-inf case. Deriving the H2 control problem and setup also serves for useful setup for the mixed H-inf/H2 optimization that the book follows up with later.

 * Objective: H2 norm
 * Variables: Controller Gains
 * Constraints: Satellite Attitude Dynamics and Kinematics. Maximum safe rotational rate of Satellite, maximum jet pulse thrust

The LMI: H-2 Satellite Attitude Control
Duan and Yu use the following H-2 Satellite Attitude Control LMI to minimize the attenuation level from disturbance to output. Note that in the H2-case we are minimizing the integral of the magnitude of the bode plot transfer function whereas in the H-inf case the optimization is minimizing the maximum value of the bode plot magnitude.

To design an optimizing controller of the form

\begin{align} u=Kx\\ \end{align}$$

such that the closed-loop system is stable and the transfer function matrix

\begin{align} G_{z2w}(s) = C_2(sI-(A+B_1K))^{-1}B_2\\ \end{align}$$

satisfies

\begin{align} \|G_{z2w}(s)\|_2 < \gamma _2\\ \end{align}$$

for a minimal positive scalar $$\gamma_2$$.

This scalar is found from the solution of the following LMI

\begin{align} \\ \min &\ \ \rho \\ \\ \text{s.t. } &AX+B_1W+(AX+B_1W)^T+B_2B_2^T < 0 \\ \\ &\begin{bmatrix} -Z & C_2X \\ (C_2X)^T & -X \end{bmatrix} < 0 \\ \\ &\operatorname{trace}(Z)<\rho \\

\text{where } \rho = \gamma_2^2.

\end{align}$$

and the controller is given by $$K = WX^{-1}$$

Conclusion:
The LMI for H-2 Satellite Attitude Control comes up with a different attenuation value for the disturbance vs the H-inf problem which is expected. It also serves for good preparation for the mixed H2/H-inf problem that Duan and Yu cover in a later section. Though no implementation is included for the mixed H2/H-inf optimization problem it is interesting to compare the results of all three cases for the satellite attitude control problem.

Implementation
A link to CodeOcean or other online implementation of the LMI

Related LMIs
Links to other closely-related LMIs

 * Hinf_LMI_SatelliteAttitudeControl
 * H2OptimalOutputFeedback
 * Full-State_Feedback_Optimal_Control_H2