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Talk:Issues in Interdisciplinarity 2020-21/Power in tax policies

Purpose of our discussion page
This section provides a space for group members to provide feedback and suggestions for improvement. It also complements our Wikibook, in the sense that it shows how we arrived at our final topic: issues of power between economics and politics in the making of tax policies. --Poccy3 (discuss • contribs) 21:52, 13 December 2020 (UTC)

First meeting 20/11/2020
In our first set up meeting for the Wikibooks chapter, unfortunately only three of us could attend, as one of us had to catch his flight back home. We started off by discussing the disciplines we want to play a role in our group project and continued with the planning of an interdisciplinary approach to them. Furthermore, we came up with a first type of research question, which was important to us in order to narrow down the research we have to do and get us a better idea of what the actual issue between these disciplines is. We came up with the research question ( Is consumer profiling through data mining, the most accurate way to predict economical behaviours? ) and chose evidence as the issue to be discussed. BonSchlonzo (discuss • contribs) 17:42, 20 November 2020 (UTC)

Second meeting 24/11/2020
This week all of us attended the meeting, therefore, we started off with a quick recap of week 1 to explain the organization and the plan with our research question to our fourth member and made our approach overall more clear. Lastly, the whole meeting was set up with the goal to break our research question down into four areas, which we then assigned to each of us in order to have a clear and easy idea of who needs to do what type of research and in which area.

The areas were the following:

For Jade: How data mining is being done? How is the data collected, processed, stored? What types of models are being used? Of what consists the quantitative evidence?

For Shervin: Behavioural Economics (How does it work, and what actually is consumer behaviour, what do firms do with it? Still with the aspect of data mining and anthropology in mind!)

For Simon: An anthropological approach to consumer profiling (what form of evidence is being used? How accurate is this in general? (in surveys, for example, people do not always say accurate statements about themselves, so how can that be accounted for, etc.)

For Niklas: How is the data that has been mined used for consumer profiling? In what sectors does this have a lot of applications? How effective is it from a business perspective?

BonSchlonzo (discuss • contribs) 10:42, 25 November 2020 (UTC)

Third meeting 27/11/2020
Everyone present at the meeting. After having discussed the findings of everyone's research (see below for a few extracts) we decided that our research question was too broad for a 1,200-word chapter. Therefore we took the decision to focus on a particular case study: Amazon's approach to consumer profiling as it is one of the biggest companies that use data-mining. This decision followed after a long debate, where we discussed the effectiveness of data-mining, its limits and contrasted it to anthropological market research.

Extracts of the research done by all members:

Jade:

From Chapter I: Introduction to Data Mining Osmar R. Zaïane, 1999 CMPUT690 Principles of Knowledge Discovery in Databases The concern in data mining are noisy data, missing values, static data, sparse data, dynamic data, relevance, interestingness, heterogeneity, algorithm efficiency, size and complexity of data. The data we have is often vast, and noisy, meaning that it’s imprecise and the data structure is complex. The Knowledge Discovery in Databases process comprises of a few steps leading from raw data collections to some form of new knowledge. The iterative process consists of the following steps:

1. Data cleaning: It is also known as data cleansing, it is a phase in which noise data and irrelevant data are removed from the collection.

2. Data integration: In this stage, multiple data sources, often heterogeneous, may be combined in a common source.

3. Data selection: At this step, the data relevant to the analysis is decided on and retrieved from the data collection.

4. Data transformation: also known as data consolidation, it is a phase in which the selected data is transformed into forms appropriate for the mining procedure.

5. Data mining: it is the crucial step in which clever techniques are applied to extract patterns potentially useful.

6. Pattern evaluation: in this step, strictly interesting patterns representing knowledge are identified based on given measures.

7. Knowledge representation: is the final phase in which the discovered knowledge is visually represented to the user. This essential step uses visualization techniques to help users understand and interpret the data mining results.

Additional Issues Quality quality of a process stands for the way a process meets users’ goals Accuracy Stored values should be compliant with real-world ones. • Freshness Data should not be old. •Completeness There should be no lack of information • Consistency Data representation should be uniform. • Availability Users should have easy access to data. • Traceability Data can easily be traced data back to its sources. • Clearness Data can be easily understood. • Accuracy and completeness

Shervin:

→ The study of economic decision making through a psychological lense, gives firms, organizations or just us as the society the chance to predict human behaviour and to understand the daily economic actions. Richard Thaler, the father of behavioural economics, came up with the whole concept of behaviour economics at first. https://hbr.org/2017/10/the-rise-of-behavioral-economics-and-its-influence-on-organizations

→ Came up with the idea of the nudge theory (positive reinforcement and indirect suggestions to lead consumer behaviour)

-https://www.ciim.ac.cy/how-to-use-nudge-theory-for-business-success/

the way we phrase things influences our thinking and forms our consumer behaviour 2008 Richard Thaler and Cass Sunstein

Prospect Theory: Kahneman and Tversky (1979)

Consumer Behaviour related to data mining:

https://arxiv.org/pdf/1109.1202.pdf

every single step we take if it is in the supermarket or on Amazon is considered a byte of data Consumer behaviour: Consumer behavior means the study of individuals, groups or organizations about their process of selecting, securing, using and disposing the products, services, experiences or ideas to satisfy needs and the impact of these processes on the consumer and the society. Application of consumer behaviour: 1. Making of marketing strategy, 2. Public Policy, 3. Consumer Common Sense (Purchasing Patterns etc.) Endowment effect: endowing someone with a good almost instantaneously makes him/her value it more highly. This can be thought of as a combination of reference dependence and loss aversion.

Simon:

consumer studies and anthropology - why and how consumers make their purchase decisions anthropological approach = more subjective and qualitative methods Abrams 2000: some cases quantitative analysis might not help decision makers to truly understand consumers WHILE “DESCRIPTIVE ANTHROPOLOGY” (qualitative and observational) provides revealing insights Thompson and Hirshman 1995: applied classic anthropology theories to study the consumers' self-conception of body images and self care practices in the modern urban society to help the marketers understand the relationship between consumer “socialized body” and consumption behavior. Mc Farlane: observes that when consumer reaction to a new product needs to be determined, companies traditionally refer to the qualitative method

Psychographic approach: define market segment through customer lifestyle lifestyle and demographics: age, location and gender activities, interest and opinions values, attitudes and social class: buying power / purchase orientation

Consumer Typology approach: consumers motivations and mindsets loyal consumers: rare but valuable → they promote brands through word of mouth discount consumers: buy if there is an “opportunity” impulsive consumers: emotionally driven rather than logically driven need-based consumers: fulfil a need

Consumer characteristics approach: traits that influence buying decisions convenience driven: need to be fast, simple and easy connectivity driven: feel connected through buying the sale product personalization driven: prefer a fully customized experience

Niklas:

“Data for the people” (2016) Andreas Weigend: describes the flaws of the current system of data collection and privacy but also discusses their potential. Advocates for more transparency of what is being done with our data, for people to become “data-literate” and for people to gain access to their data profiles and give them the possibility to change/blur data about them. The more data a company collects about an individual, the more targeted advertising can it bring to them. For example Alphabet collects an enormous amount of personal information through Google Maps (where you shop, work, live, what places you often visit) and this information is on a database, also accessible by Google (search) that can then give you targeted advertising according to the collected information.

source: https://books.google.co.uk/books?id=pKtVDgAAQBAJ&printsec=frontcover&dq=data+for+the+people&hl=fr&sa=X&ved=2ahUKEwjT99uh16LtAhVSXMAKHeNbCtYQ6AEwAHoECAYQAg#v=onepage&q=amazon&f=false “The Internet Encyclopedia” (2004) Hossein Bidgoli: Consumer profiling based on data mining is divided into two main categories: factual and rule-based. The factual profile is the information given by a customer by actively giving the information to the company (address, name, gender, etc) while rule-based profiles are designed after tracking and then analyzing the activity of the user on the Internet.

Looking further at the rule-based profiling, there are two steps in determining such a profile: rule-discovery and rule validation. → Agrawal et al. (1998): multidimensional indexing structure and algorithm for mining profile association rules. consumer-profiling is very closely linked to recommendation systems developed by the companies. Two common techniques in developing recommendation systems are item-to-item correlation (recommending similar items) and customer-to-customer correlation (recommending products that other customers who bought the same product, bought after that).

--Poccy3 (discuss • contribs) 17:28, 27 November 2020 (UTC)

Fourth meeting 02/12/2020
In this meeting, Simon and Jade discussed the main features of Evidence of Amazon’s consumer profiling. The main purpose of this meeting was to come together with the key point to address in the Wikibook chapter. The subject seemed again very broad and it was difficult for them to find any problem with Amazon’s algorithms. They mainly talked about how dangerous it can be for society to read the same books or buy the same products. Amazon algorithms are using a ranking system to recommend product and an uniformization of ideas could be a great way to generate profit. They realized that maybe it was better to address the issue of Amazon through the lens of Power rather than Evidence.

Fifth meeting 04/12/2020
On Friday, the whole team met on Zoom during almost 3 hours. They discussed the change of issue suggested draws their attention on Amazon monopoly in e-commerce. They come together with the new following question and main points:

To what extent should politics interfere with Amazon’s power in e-commerce?

Introduction: (200 words) Shervin

Define power of firms in economics and show why Amazon is a firm with a lot of economic power. How politics can regulate the economic market, in regards to the research question.

First part: (400 words) SIMON and SHERVIN

Show how Amazon has an unfair advantage compared to their competition. Argue that this gives Amazon a quasi-monopoly over e-commerce. Explain why monopolies are bad for economics. Define Monopoly and what a monopoly means for the economy/politics.

Second part: (400 words) NIKLAS and Jade

Discuss how wealth can be redistributed and Amazon’s economic power limited through laws and regulations. Power conflict between Regulators and Amazon. Lobbyists will try to maintain Amazons power.

Sixth meeting 08/12/2020
After a quick talk with James during our tutorial, we realised that our interdisciplinary example was not focused enough on the disciplines.So, we decide to meet again to redefine our research. You can find below the main features we agreed to discuss. As time flies, we also agreed to meet again on Friday to edit our work together.

Final research question: Why do political scientists and economists disagree on redistributive policies?

Intro (200 words) Shervin: what are redistributive policies? What kind of approaches exist? Outline its failure in the past 100 years and mention that it is getting worse. (Case Study)

Part 1 : Oppose wealth redistribution in the two disciplines (600 words) in Economics : Simon how economics approach wealth redistribution ? Main concepts → power, economic growth, how do we measure wealth redistribution? limits ? Amazon example of economist approach (they don’t pay taxes almost) in Politics : Jade concepts → equality, power restriction, can an egalitarian/equal growth exist ? policies proposed to limit Amazon with new redistributive policies

Part 2: How both can be reconciled? Is it even possible? new approach to growth (200 words) Niklas Should we privilege growth over people? Does growth = more wealth among different classes? Doughnut economics → by looking at other things than growth (for example happiness, HDI, health care etc.) we can have a more equal society Should we reconsider how we measure wealth redistribution ? Conclusion:

Seventh meeting 11/12/2020
We met again and everyone received feedback on their work. Everyone agreed with the final version of our Introduction; we agree that everyone should focus more on our issue Power. We also set our last meeting for next Sunday and here are the following points, we agreed to discuss for next meeting:

-conclusion where are we heading ?????

-work on titles

-check uniformity références

-write meeting 8

- post more on discussion page —> link new doc / readings.

- words count

Jadaireu (discuss • contribs) 14:44, 13 December 2020 (UTC)

Eighth meeting 13/12/2020
In our eighth meeting, we all went over our respective final versions of our paragraphs and came to agreements on phrasing, references, titles, and content.

For the conclusion, we decided to write each their own and ended up merging Simons's conclusion with mine as they complemented well.

We agreed to meet one last time on 14/12/2020, to check the last, complete version of our work, before submitting it. --Poccy3 (discuss • contribs) 20:46, 13 December 2020 (UTC)

Nineth meeting 14/12/2020
In todays meeting, we all came together one last time to make sure we are all in line and consistent with our references, font etc. Finally we made a last change in updating the title of our Wikibooks chapter. Having had the complete version which we are were very satisfied with, we submitted it onto Moodle and Wikibooks.

BonSchlonzo (discuss • contribs) 15:49, 14 December 2020 (UTC)

Readings
This section brings together our most interesting readings :

===Amazon.com Recommendations === This article describes how Amazon's algorithms work. Amazon uses item-to-item collaborative filtering algorithms. In basic words, those algorithms find a set of customers who purchased and rated items and will recommend those items to similar customers.

Two main algorithms are used :

- the collaborative filtering

This algorithm assign to a customer a vector with N-dimensions. N represents the number of distinct items. In order to find the best selling items, the vector assigned will be multiplied by the inverse frequency (= inverse of the number of customers who have purchased or rated the item). As you can imagine, this method is computationally expensive. In order to process the data, we need to reduce our data set. For example, we can reduce N by eliminating some very unpopular items of the catalog. If we reduce our data size, we also reduce our recommendation quality.

- cluster model

The goal of this algorithm is to regroup people from the same interests. To do so, "cluster models divide the customer base into many segments and treat the task as a classification problem." The algorithm starts with an initial data set of segments and repeatly match customers together to obtain the optimal cluster.

===Introduction to Data Mining ===

This chapter was very helpful to understand how data we have is often noisy and hard to process. It also describes the different steps in Knowledge Discovery. A very useful process to understand how information is extracted. This method regroups 7 fundamental steps such as the data cleaning or pattern evaluation. Data Mining is one of this step and is composed of five major elements. Very briefly we first, extract and store the data. Then, we provide data access to analysts and analyze it through a specific software. The last step consists in the presentation of the data in a useful format ( graph, table,...) A variety of data sources can be mined. ( flat files, time-series files, relational databases,...)

An interesting point of this chapter was the challenging problems in data mining. Indeed, data mining raises multiples issues : Security and social issues, User interface issues, Data source issues,Performance issues,... I strongly believe that such issues need an interdisciplinary approach to be solved or at least clearer.

NB: An other problem : most algorithms assume the data to be noise-free. This is of course a strong assumption.

===The Politics of Wealth Taxes === This document showed how 'democracy has proven relatively gentle with the wealthy'( ,1). It underlined the different approach to the Politics of Wealth Taxes. It stressed the fact that citizens are misinformed about the world of taxation and how private groups in specific country (such as Britain, France, Ireland, ...) use this indifference to their advantages.

A theory of Justice
"John Rawls’s A Theory of Justice has become a classic. Rawls aims to express an essential part of the common core of the democratic tradition—justice as fairness—and to provide an alternative to utilitarianism, which had dominated the Anglo-Saxon tradition of political thought since the nineteenth century. Rawls substitutes the ideal of the social contract as a more satisfactory account of the basic rights and liberties of citizens as free and equal persons. “Each person,” writes Rawls, “possesses an inviolability founded on justice that even the welfare of society as a whole cannot override.” Advancing the ideas of Rousseau, Kant, Emerson, and Lincoln, Rawls’s theory is as powerful today as it was when first published."

===Campaign Contributions Facilitate Access to Congressional Officials: A Randomized Field Experiment ===

This article provided one of the bases for arguing, that in the US, the wealthy have an important influence on policy-making, made possible through the American campaign system. The latter, where candidates rely on donations from donors (be it corporate groups or individuals) has established a system, in which there is a resource dependency of candidates to donors.

Google Docs
We shared our research through Google Docs. It was easier for us to see the progress of our work. Here is the link to our Google Docs : https://docs.google.com/document/d/1XyKnycFOmTlx83aZF7opmkoUpopARpAw_rwnu63BV2Y/edit?usp=sharing

@Everbody

Hopefully you guys will read it, but should we really leave the link for our google doc here as our google doc is more for our general notes etc. and is not really beneficial to anyone marking our essay. Please just let me know below or in our WhatsApp group so we can discuss this.

BonSchlonzo (discuss • contribs) 18:28, 12 December 2020 (UTC)

I agree Shervin, the Google Doc is very messy as well, so I'm not sure if the corrector will be able to easily navigate through that document and understand how we went from one topic to the other. I would not include it here. --Poccy3 (discuss • contribs) 21:43, 13 December 2020 (UTC)

I don't think they will check it but if they do, it won't be detrimental toward our final grade. If you want to delete the section, you can.

Jadaireu (discuss • contribs) 9:33, 14 December 2020 (UTC)

@Jadaireu & @Poccy3 I have thought about it again and came to the conclusion, that our grade will not be any reflection of our google document, therefore, I think we should actually leave it here to show which tool we used to aid our collaborative work outside of the WikiBooks page. I hope you all agree with me, this is just a very last detailed comment, which we can quickly discuss in our last meeting later on today, just before submitting.

BonSchlonzo (discuss • contribs) 12:22, 14 December 2020 (UTC)

Introduction
Hi guys, just read these two super interesting articles, which exactly relate to our issue and topic, have a read through if you like. https://eml.berkeley.edu/~saez/SaezZucman14slides.pdf https://www.un.org/esa/desa/papers/2015/wp139_2015.pdf

BonSchlonzo (discuss • contribs) 18:01, 10 December 2020 (UTC)

idea for opening question: https://www.youtube.com/watch?v=pTwPHuE_HrU American wealth inequality is staggering. A wealth tax, which would hone in on the money people actually have, rather than just the money we earn and spend, could be a solution!

Uclqevy (discuss • contribs) 18:20, 10 December 2020 (UTC)

Shervin, your articles are exactly the kind of papers that are perfect for this discussion thanks! The figure that the 85 richest individuals own as much wealth as the poorest half of the population is what struck me the most. Maybe we can start off with that in the introduction and then use the information from Simons video as a transition to show we're focussing on the US. Or should we start off immediately with the US? --Poccy3 (discuss • contribs) 17:39, 10 December 2020 (UTC)

@Poccy3, I have just uploaded the second draft of my introduction, have a look at the bottom, I have just very briefly mentioned the US as a little introduction and one quick fact about the wealth distribution in the US. I think that makes the transition easier to Simon's video.

BonSchlonzo (discuss • contribs) 19:10, 10 December 2020 (UTC)

First part
Jade for your part on the concept of wealth redistribution in politics/political science I think it could be pertinent to look at John Rawls' work, especially the justice as fairness concept, which we could use as an opposite to how rich people/ big companies are being taxed nowadays. I'm happy to try and find relevant articles if you think you have the space to include this! --Poccy3 (discuss • contribs) 02:16, 10 December 2020 (UTC)

okay i'm going to read about it today. Thank you !!! I will let you know what I think about his work Jadaireu (discuss • contribs) 10:06, 10 December 2020 (UTC)

Jade, about the meaning of wealth distribution, I think there is a way to identify a main general concept to the two disciplines which are economics and politics and then illustrate the term "growth" related to an economical perspective, and " equal distribution" to a political perspective.

Uclqevy (discuss • contribs) 14:13, 10 December 2020 (UTC)

Simon, I really like what you have written it is really concise and goes exactly towards the point we want to make. However, I just have on little suggestion, when you mention the effect of the tax and explain it in your two middle paragraphs, could you point out the issue of power a little bit more there, as I think it makes it then easier to see the real issue when reading through jade's second part of the argument.

BonSchlonzo (discuss • contribs) 19:15, 10 December 2020 (UTC)

Mr BonSchlonzo, thank you for your comment! I agree, it will improve the understanding of the orverall, the text will be better articulated.

Uclqevy (discuss • contribs) 21:22, 10 December 2020 (UTC)

Jade your paragraphs are really good and really well written but to save some space I would go straight to the second paragraph (we can probably include the first part in the intro or elsewhere) and we need to be more clear about why power is the issue between economics and politics. you could talk about the influence of journalism, public awareness, NGOs that fight for the ‘common people’ and try to mobilize people, to fight the lobbyists working for the rich. Something like how powerful political scientists are at influencing policymaking in general.

Simon, your paragraph is really good as well but to contrast it with Jade's more you could talk about lobbying first, which influences policy-making a lot. You can give examples of spending of big tech companies on lobbying (the numbers are remarkable). --Poccy3 (discuss • contribs) 11:16, 11 December 2020 (UTC)

Yes I agree Poccy3, Jade you can have a look, I deleted the Amazon example and add a final part on lobbying. Thus the transition with your political arguments could work pretty well... starting with this famous "quote". Have a look and tell me. Thank you Poccy3, for your very good remark.

Uclqevy (discuss • contribs) 18:22, 11 December 2020 (UTC)

@Poccy3 hey I read your message and i totally agree with you. we already discussed it during our morning so no need to answer for me to your message really

@uclqevy Great job ! I find your part super go straight to the essential ( aie aie I'm really losing my english). I believe taht the part on lobbying was really essential to our problem and really show the power that economists have in our society. Economists use their principles and arguments on growth to distribute wealth and implement public policies, which only benefit a small fraction of the population.so bravo !I still don't really understand the sentence " allow the concept of neutrality ". I am probably very tired but your sentences are so long (maybe put more dote )

@everybodyyy I upload my second draft. Can someone please rephrase my sentence on the American Dream ? Please tell me if I need to rewrite it. With no that much words, I just have the impression that what I wrote is just super unclear and that I missed so many interesting points to make.

Jadaireu (discuss • contribs) 19:25, 11 December 2020 (UTC)

@Jadaireu I think your part is now almost perfect, I added a few suggestions for rephrasing on the Google docs but other than that its perfect! --Poccy3 (discuss • contribs) 10:00, 12 December 2020 (UTC)

Second part
Hi Niklas,

I really like what you wrote. I can clearly see why economics and political scientists can not agree on the subject of wealth redistribution and how biased the subject of taxation is. Indeed, I also believe that governments should inform more their citizens on the use of their taxes. This part should show that the tension that exists between economists and political scientist on the subject of wealth redistribution is strongly related to the issue of Power. Maybe the word of power should be use ? Or should we link our example to Power in the conclusion ? What do you think ?

Could you also check your references ? I don't know who wrote your article for the reference 14 and as expressed in the assignement instructions, all quotes must follow the Vancouver system : https://en.wikipedia.org/wiki/Vancouver_system

I hope my feeback will help you ! I still have a lot to do to obtain the same writing quality of your paragraph :))

Jadaireu (discuss • contribs) 10:30, 10 December 2020 (UTC)

Hi Jade, thanks for the flattering words and suggestions I appreciate it! I only mentioned power at the beginning of the paragraph because my goal was to try to find common ground between the two disciplines to overcome the issue and that the conflict over power will be already clear after the first part. I still think it would be good to mention it more clearly though, so I will work on that. One little problem is that my part is already longer than what we said in the outline, so I will either have to cut smth or we need to write a shorter introduction/conclusion. Let me know what you think is the best option. --Poccy3 (discuss • contribs) 10:52, 10 December 2020 (UTC)

Hi jade, here is a proposition for the syntax of your sentence about the willignness of Americans to succeed: Americans have a strong belief in the principle of meritocracy; being rich only depends on your willingness whereas European see poverty as an economic scourge where high taxes would rebalance the injustice.

--Uclqevy (discuss • contribs) 22:44, 11 December 2020 (UTC)

@everyone As I suggested on our WhatsApp group, I will change the second part to focus on the issue of power in the process of tax regulations, since the first part brings more detail to how both our disciplines approach taxation but don't exactly point out the issue between them. Maybe the current second part can be an inspiration for the conclusion, particularly the point on doughnut economics. Lmk what you think of that!

conclusion ?
Hello everyone !

I found this quote. I think that it illustrates well how hard it is for politicians to tax big companies.

What do you think about it ??

"Either you are creating jobs or you are losing jobs. Either you are part of the economy of tomorrow or you are part of the economy of yesterday. This is a competition and for us, it’s about being part of the economy of tomorrow."(A.Cuomo,2018).

Jadaireu (discuss • contribs) 23:03, 7 December 2020 (UTC)

Hi Jade, I do agree that this quote illustrates one problem of taxing big companies (since they employ a lot of people they have a lot of economical power) but in my opinion, we should focus on potential solutions to overcome this problem in the conclusion. If there is enough space, I think this quote might be better suited for the first part. Let me know what you think! --Poccy3 (discuss • contribs) 16:33, 9 December 2020 (UTC)

Hello, Yes I agree this quote is not very relevant with our subject !!! It was mostly for our draft on Amazon's power. I'm going to delete it. I think we could find a better quote :)

Jadaireu (discuss • contribs) 9:51, 10 December 2020 (UTC)

Hey guys, in the conclusion one thing we could do is mention countries, where policy-making has gone past focussing on growth, to show that there can be a discussion between economists and political scientists that works. This article shows how New Zealand has abandoned GDP growth as its main indicator and now focuses on well-being and happiness: https://www.forbes.com/sites/jamesellsmoor/2019/07/11/new-zealand-ditches-gdp-for-happiness-and-wellbeing/?sh=2e0d0b9d1942 Do you think that would be a good way to tackle the conclusion? --Poccy3 (discuss • contribs) 21:35, 10 December 2020 (UTC)

Hi Niklas, I think its one good way to approach the conclusion, where we could also most definitely mention countries like Norway, Finland and Sweden which all follow kind of the same principle. Another idea I had was instead of still focusing just on the US, contrast two countries which handle redistribution completely differently and which may focus on other taxes which don't have such a major impact on the economy and its growth. and give some facts/stats about their economic and political goals, in order to give an idea which does better in terms of what! Here is an article I found for Norway: https://www.ecnmy.org/engage/norways-the-happiest-country-on-earth-be-more-norway/

BonSchlonzo (discuss • contribs) 11:56, 12 December 2020 (UTC)

@BonSchlonzo I completely agree with you! As we primarily focus on the effect of raising the corporate tax, we could talk about raising income tax for the super rich, bigger property taxes, or just generally that by designing taxes that target the wealth of individuals rather than their income can be more efficient and fair (because a lot of super-rich like Jeff Bezos don't earn most of their wealth through income but through other means that are even less taxed). --Poccy3 (discuss • contribs) 21:38, 13 December 2020 (UTC)

Exactly, it should be fairly easy to reflect that onto a different country maybe other than the US. If you have a look at thus link I just found which talks about proportional and progressive taxes and how they impact economic growth but also the redistribution of wealth. https://dash.harvard.edu/bitstream/handle/1/2799054/feldstein_statetaxes.pdf;jsessionid=EA044AA35497699469FF5A897E62AB44?sequence=4

BonSchlonzo (discuss • contribs) 12:08, 12 December 2020 (UTC)

Good evening BonSchlonzo and Poccy3, here is a good source from Vox to speak about a new way to tax the super rich https://www.vox.com/videos/2019/3/5/18249732/wealth-tax-better-way-rich. It seems to me a good opening on the conflict of this chapter. Uclqevy (discuss • contribs) 18:06, 12 december 2020 (UTC)

hello everyone, you might not agree with me but i think we should conclude by highlighting how our believes, the fact that some scholars take their knowledge as granted and only see the world through their own discipline can be damaging. People should more be careful with their own concepts and should challenge their perceptions of wealth.

Jadaireu (discuss • contribs) 23:03, 18:24 12 December 2020 (UTC)

@Jadaireu sorry to only get back to you now. Have a look at our final version of the conclusion and tell us if you would change anything about it! --Poccy3 (discuss • contribs) 21:38, 13 December 2020 (UTC)

For the proofreader's information, we have merged our findings into a single conclusion. Poccy3 and Uclqevy Uclqevy (discuss • contribs) 10:21, 14 December 2020 (UTC)

@Poccy3 & @Uclqevy I went through it yesterday and the conclusion is super ! Great job ! Jadaireu (discuss • contribs) 9:36, 14 December 2020 (UTC)

@Poccy3 &Uclqevy, I just went over the whole text again and I am really happy with the whole outcome, the idea we had to merge the conclusions of the both of you was important to ensure a smooth ending with the right meaning!

BonSchlonzo (discuss • contribs) 11:51, 14 December 2020 (UTC)

Very good job everyone !!! What a journey it has been :). It has been a pleasure to work with you.I remember I was going to work on the definition of gender in our society and finally, i contributed to the topic of the US corporate law, which we started as Evidence in consumer profiling. I hope that you are all happy with our final version !

Merry Christmas guys !

See you soon

Jadaireu (discuss • contribs) 11:08, 14 December 2020 (UTC)

readings?
Hi I create a section 'Readings' in our discussion page to share our most interesting readings. What do you think ? Should we also add some quotes ?

Jadaireu (discuss • contribs) 10:37, 7 December 2020 (UTC)

Hi Jade,

I like your Idea I wouldn't only add quotes and your most interesting readings, maybe just add most of your sources, so if anyone struggles to find an appropriate source, we can always come back to our readings here. Top idea, thanks.

BonSchlonzo (discuss • contribs) 16:52, 9 December 2020 (UTC)

Hi Jade, I also think the idea is really good! I will add my readings to the section and I also believe quotes can be useful to this section, if that quote illustrates well, why that reading was relevant to our research. Also, do you think it would be helpful to add other sources than readings (videos, podcasts...) or not? --Poccy3 (discuss • contribs) 16:33, 9 December 2020 (UTC)

Yes I agree guys ! We should add any type of source we came accross during our research. I believe that the purpose of the discussion page is to show the evolution of our thoughts (how we shape our ideas for the chapter) so I believe that any source that we find interesting on any subject is still valuable.

@Poccy3 Why do you think we should only put in this section the sources related to our subject ? @Jadaireu Nevermind I agree showcasing how we came to the problem of wealth distribution is actually interesting to understand how we tackled the problem! Good idea to keep the entire train of thought.

@BonSchlonzo I believe we can find every source on the reference page of our chapter. That's why, I think that it will be more relevant to put 2/3 readings or videos/podcast that helped us define our ideas or that we think are very valuable for a future project.

Jadaireu (discuss • contribs) 9:57, 10 December 2020 (UTC)

144.82.9.19
Hey everyone ! I just wanted to mention that if you see 144.82.9.19 in the discussion tab, it is me. Sometimes, I edit our wikibook and forget to log on. So no worries ! Jadaireu (discuss • contribs) 19:19, 11 December 2020 (UTC)

Ok, good to know, don't think I've seen it so far but thanks for letting us know! --Poccy3 (discuss • contribs) 20:47, 13 December 2020 (UTC)


text:
User talk:Bascrzm

yes it's perfect good job girls !!

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

title: User talk:Bascrzm


text:
User talk:41.77.91.207

Welcome!
Hello, and welcome to Wikibooks! Thank you for your contributions so far. I hope you like the place and decide to stay.

You don't have to log in to read or edit articles on Wikibooks, but if you wish to acquire additional privileges, you can simply  [ create a named account] . It's free, requires no personal information, and lets you:
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If you edit without using a named account, your (41.77.91.207) is used to identify you instead.

By the way, please make sure to sign and date your talk page comments with four tildes (~~~~).

Happy editing! —Atcovi (Talk - Contribs) 16:09, 14 December 2020 (UTC)

title: User talk:41.77.91.207


text:
User:Milanand/Python 3 Programming/Strings

Overview
Strings in Python at a glance:

Equality
Two strings are equal if they have exactly the same contents, meaning that they are both the same length and each character has a one-to-one positional correspondence. Many other languages compare strings by identity instead; that is, two strings are considered equal only if they occupy the same space in memory. Python uses the  operator to test the identity of strings and any two objects in general.

Examples:

Numerical
There are two quasi-numerical operations which can be done on strings – addition and multiplication. String addition is just another name for concatenation. String multiplication is repetitive addition, or concatenation. So:

Containment
There is a simple operator  that returns True if the first operand is contained in the second. This also works on substrings: Note that  would have also returned the same value.

Indexing and Slicing
Much like arrays in other languages, the individual characters in a string can be accessed by an integer representing its position in the string. The first character in string  would be   and the nth character would be at. Unlike arrays in other languages, Python also indexes the arrays backwards, using negative numbers. The last character has index -1, the second to last character has index -2, and so on. We can also use "slices" to access a substring of. will give us a string starting with  and ending with. None of these are assignable. Another feature of slices is that if the beginning or end is left empty, it will default to the first or last index, depending on context: You can also use negative numbers in slices: Element:    1     2     3     4 Index:   0     1     2     3     4 -4   -3    -2    -1 So, when we ask for the [1:3] slice, that means we start at index 1, and end at index 2, and take everything in between them. If you are used to indexes in C or Java, this can be a bit disconcerting until you get used to it.

String constants
String constants can be found in the standard string module. An example is, which equals to.

Links for further reference:

 * Python Documentation on String module

String methods
There are a number of methods or built-in string functions:

Only emphasized items will be covered.

is*
,,  ,  ,  ,  , and   fit into this category.

The length of the string object being compared must be at least 1, or the is* methods will return False. In other words, a string object of, is considered "empty", or.

 *   returns  if the string is entirely composed of alphabetic and/or numeric characters (i.e. no punctuation).
 *   and   work similarly for alphabetic characters or numeric characters only.
 *   returns  if the string is composed entirely of whitespace.
 *  ,  , and   return  if the string is in lowercase, uppercase, or titlecase respectively. Uncased characters are "allowed", such as digits, but there must be at least one cased character in the string object in order to return  . Titlecase means the first cased character of each word is uppercase, and any immediately following cased characters are lowercase. Curiously,   returns True. That is because uppercase characters can only follow uncased characters. Likewise, lowercase characters can only follow uppercase or lowercase characters. Hint: whitespace is uncased.

Example:

title, upper, lower, swapcase, capitalize
Returns the string converted to title case, upper case, lower case, inverts case, or capitalizes, respectively.

The   method capitalizes the first letter of each word in the string (and makes the rest lower case). Words are identified as substrings of alphabetic characters that are separated by non-alphabetic characters, such as digits, or whitespace. This can lead to some unexpected behavior. For example, the string  will be converted to   instead of.

The   method makes all uppercase letters lowercase and vice versa.

The   method is like title except that it considers the entire string to be a word. (i.e. it makes the first character upper case and the rest lower case)

Example:

count
Returns the number of the specified substrings in the string. i.e. Hint:  is case-sensitive, so this example will only count the number of lowercase letter  s. For example, if you ran:

strip, rstrip, lstrip
Returns a copy of the string with the leading and trailing  whitespace removed. removes both. Note the leading and trailing tabs and newlines.

Strip methods can also be used to remove other types of characters. Note that  and   require an   statement

ljust, rjust, center
left, right or center justifies a string into a given field size (the rest is padded with spaces).

join
Joins together the given sequence with the string as separator:  may be helpful here: (it converts numbers in   into strings) Now arbitrary objects may be in   instead of just strings.

find, index, rfind, rindex
The  and   methods return the index of the first found occurrence of the given subsequence. If it is not found,  returns   but   raises a. and  are the same as   and   except that they search through the string from right to left (i.e. they find the last occurrence) Because Python strings accept negative subscripts, index is probably better used in situations like the one shown because using find instead would yield an unintended value.

replace
works just like it sounds. It returns a copy of the string with all occurrences of the first parameter replaced with the second parameter. Or, using variable assignment: Notice, the original variable remains unchanged after the call to.

expandtabs
Replaces tabs with the appropriate number of spaces (default number of spaces per tab = 8; this can be changed by passing the tab size as an argument). Notice how (although these both look the same) the second string (t) has a different length because each tab is represented by spaces not tab characters.

To use a tab size of 4 instead of 8: Please note each tab is not always counted as eight spaces. Rather a tab "pushes" the count to the next multiple of eight. For example:

split, splitlines
The   method returns a list of the words in the string. It can take a separator argument to use instead of whitespace. Note that in neither case is the separator included in the split strings, but empty strings are allowed.

The   method breaks a multiline string into many single line strings. It is analogous to  (but accepts   and   as delimiters as well) except that if the string ends in a newline character,   ignores that final character (see example). The method   also accepts multi-character string literals:


text:
User:Milanand/Python 3 Programming/Conditional Statements

Decisions
A Decision is when a program has more than one choice of actions depending on a variable's value. Think of a traffic light. When it is green, we continue our drive. When we see the light turn yellow, we reduce our speed, and when it is red, we stop. These are logical decisions that depend on the value of the traffic light. Luckily, Python has a decision statement to help us when our application needs to make such decision for the user.

If Statements
Here is a warm-up exercise - a short program to compute the absolute value of a number: What does the computer do when it sees this piece of code? First it prompts the user for a number with the statement. Next it reads the line. If  is less than zero Python runs the line. Otherwise Python runs the line.

More formally, Python looks at whether the expression  is true or false. An  statement is followed by an indented block of statements that are run when the expression is true. After the  statement is an optional   statement and another indented block of statements. This 2nd block of statements is run if the expression is false.

Expressions can be tested several different ways. Here is a table of all of them: Another feature of the  command is the   statement. It stands for "else if," which means that if the original  statement is false and the   statement is true, execute the block of code following the   statement. Here's an example: Notice how the   is only tested when the   statement fails to be true. allows multiple tests to be done in a single if statement.


text:
User talk:75.83.64.170

Warning
—Atcovi (Talk - Contribs) 17:22, 14 December 2020 (UTC)

Responding
Won’t vandalize.

I will not edit
I do not want to get blocked.

title: User talk:75.83.64.170


text: User:Milanand/Python 3 Programming/Conditional Statements/Solution


text:
Free Knowledge Culture Calendar/May 18

Today in 2010 RISC-V was started.

title: Free Knowledge Culture Calendar/May 18


text:
User:Milanand/Python 3 Programming/Loops

While loops
This is our first control structure. Ordinarily the computer starts with the first line and then goes down from there. Control structures change the order that statements are executed or decide if a certain statement will be run. As a side note, decision statements (e.g.,  statements), which you learned about in the last chapter, also influence whether or not a certain statement will run. Here's the source for a program that uses the  control structure: So what does the program do? First it sees the line  and sets the variable a to zero. Then it sees  and so the computer checks to see if. The first time the computer sees this statement,  is zero, and zero is less than 5. In other words, while a is less than five, the computer will run the indented statements.

Here is another example of the use of : Notice how   is only run at the end. The  statement only affects the lines that are tabbed in (a.k.a. indented). The  means does not equal so   means until   is zero run the tabbed in statements that are afterwards.

Now that we have  loops, it is possible to have programs that run forever. An easy way to do this is to write a program like this: This program will output  until the heat death of the universe or until you stop it. The way to stop it is to hit the Control (or Ctrl) button and `c' (the letter) at the same time. This will kill the program. (Note: sometimes you will have to hit enter after the Control C.)

For loops
The next type of loop in Python is the  loop. Unlike in most languages,  requires some   object like a   or   to work. The output looks very familiar, but the program code looks different. The first line uses the  function. The  function uses two arguments like this. is the first number that is produced. is one larger than the last number. Note that this program could have been done in a shorter way: Here are some examples to show what happens with the  function: Another way to use the   function in a   loop is to supply only one argument: The above code acts exactly the same as: with 0 implied as the starting point.

The code would cycle through the  loop 10 times as expected, but starting with 0 instead of 1.

The next line  uses the   control structure. A  control structure looks like. is gone through starting with the first element of the list and going to the last. As  goes through each element in a list it puts each into. That allows  to be used in each successive time the for loop is run through. Here is another example to demonstrate: Notice how the for loop goes through and sets item to each element in the list. (Notice how if you don't want  to go to the next line add a comma at the end of the statement (i.e. if you want to print something else on that line). ) So, what is   good for? The first use is to go through all the elements of a list and do something with each of them. Here a quick way to add up all the elements: Or you could write a program to find out if there are any duplicates in a list like this program does: How does it work? Here is a special debugging version: Note: The reason there are so many  calls is because   calls can show the value of each variable at different times, and help debug the program. First the program starts with the old list. Next the program sorts the list. This is so that any duplicates get put next to each other. The program then initializes a (ious) variable. Next the first element of the list is deleted so that the first item is not incorrectly thought to be a duplicate. Next a  loop is gone into. Each item of the list is checked to see if it is the same as the previous. If it is a duplicate was found. The value of  is then changed so that the next time the for loop is run through   is the previous item to the current. Sure enough, the  is found to be a duplicate.

The other way to use  loops is to do something a certain number of times. Here is some code to print out the first 9 numbers of the Fibonacci series: Everything that can be done with  loops can also be done with   loops but   loops give an easy way to go through all the elements in a list or to do something a certain number of times.

The break statement
A -loop checks its termination condition before each entry into the loop body, and terminates if the condition has gone. Thus, the loop body will normally iterate zero, one or more complete times.

A -loop iterates its body once for each value returned from the iterator expression. Again, each iteration is normally of the complete loop body.

Sometimes you want to conditionally stop the loop in the middle of the loop body. An example situation might look like this:

 * 1) Obtain the next value to check
 * 2) Is there in fact another value to check? If not, exit the loop with failure.
 * 3) Is the value what I'm looking for? If so, exit the loop with success.
 * 4) Otherwise, go back to step 1.

As you can see, there are two possible exits from this loop. You can exit from the middle of a Python - or  -loop with the  -statement. Here is one way to write such a loop: The trouble with this is the asymmetry between the two ways out of the loop: one through normal -loop termination, the other through the. As a stylistic matter, it would be more consistent to follow this principle:

 * If one exit from a loop is represented by a, then all exits from the loop should be represented by  s.

In particular, this means that the loop construct itself becomes a “loop-forever” loop; the only out of it is via a  statement.

We can do this by explicitly dealing with an iterator yielding the values to be checked. Perhaps the  call above already yields an iterator; if not, it can be converted to one by wrapping it in a call to the   built-in function, and then using the   built-in function to obtain successive values from this iterator. Then the loop becomes: This uses the special Python value  to indicate that the iterator has reached its end. This is a common Python convention. But if you need to use  as a valid item in your sequence of values, then it is easy enough to define some other unique value (e.g. a custom dummy class) to represent the end of the list.

Exercise

 * 1) Write a programme that computes and prints the 1000th prime number.

Solution


text:
User talk:Эарендил

Hello Эарендил, your edits to Oberon/ETH Oberon/faqlang produced no damage that I could see. Consequently I switched back to your version. Several more files are yet to review. A benefit of the automated editing and reviewing is that I've corrected some subtle errors and updated hyperlinks where the target wasn't available when the link was created. Regards, ... PeterEasthope (discuss • contribs) 19:29, 14 December 2020 (UTC)

title: User talk:Эарендил