Noise

The world is full of noise. This is not a novel insight. Luckily, the power of statistical models to predict human behaviour is limited, and if a model is able to predict all relevant variation in an outcome of interest, we should be concerned about overfitting and other potential problems. There are good reasons why R² is rarely anywhere near 1 in regression outputs and why regularization in machine learning is such a hot topic.

Noise is part of the error term in our statistical models. A good model is not trying to explain everything about the world (and not have any noise at all). Unsurprisingly, within the domain of behavioural economics, the idea of noise as a core component of human behaviour has been known for decades. Humans are not (fully predictable) machines, partially because there are limits to how we process information. The concept of bounded rationality, introduced by Herbert Simon, is one good example of how we can understand the existence of noise.

The psychologists Daniel Kahneman and Amos Tversky improved our understanding of human behaviour by convincingly demonstrating that a lot of what goes into the error term, i.e., what we treat as noise, is not in fact noise but predictable behaviour rooted in cognitive biases. Here is how the economist Richard Thaler describes this historical development in his book, Misbehaving: The Making of Behavioural Economics:

I believe many economists ignored [Herbert] Simon because it was too easy to brush aside bounded rationality as a “true but unimportant” concept. Economists were fine with the idea that their models were imprecise and that the predictions of those models would contain error. In the statistical models used by economists, this is handled simply by adding what is called an “error” term to the equation. Suppose you try to predict the height that a child will reach at adulthood using the height of both parents as predictors. This model will do a decent job since tall parents tend to have tall children, but the model will not be perfectly accurate, which is what the error term is meant to capture. And as long as the errors are random—that is, the model’s predictions are too high or too low with equal frequency—then all is well. The errors cancel each other out. This was economists’ reasoning to justify why the errors produced by bounded rationality could safely be ignored. Back to the fully rational model! Kahneman and Tversky were waving a big red flag that said these errors were not random. Ask people whether there are more gun deaths caused by homicide or suicide in the U.S., and most will guess homicide, but in fact there are almost twice as many gun deaths by suicide than homicides. This is a predictable error.

Over the years, thousands of studies have tried to remove noise from the models and give the predictable errors unique names, often in the form of specific cognitive biases. Today, behavioural economics is a thriving research agenda uncovering too many predictable errors (biases) to cover in a blog post (or a book for that matter!). In his book from 2011, Thinking, Fast and Slow, Daniel Kahneman summarised a lot of the research within the field (alas, a lot of this research relied on noisy data and, accordingly, does not hold up well).

Three years prior to the publication of Thinking, Fast and Slow, the book Nudge: Improving Decisions about Health, Wealth, and Happiness, by Richard Thaler and Cass Sunstein, brought behavioural economics to the policy-makers toolbox. The basic principle is that traditional economic models of human behaviour will not be sufficient if we are to reach policy objectives. In other words, there is too much noise generated by traditional models that behavioural economics can help us understand – and even take advantage of if we are to improve decisions about … health, wealth, and happiness.

However, despite decades of research on minimising the error term, i.e., shed light on the predictable errors in human judgments and decisions, the fact remains that there is still a lot of noise in human judgments. But maybe we focus too much on what we can predict and too little on noise? This is the topic of the new book, Noise: A Flaw in Human Judgment, by Daniel Kahneman, Olivier Sibony and Cass Sunstein. In other words, the book is not trying to simply understand biases, but this thing called noise. Or, as the authors introduce the focus of the book: “The topic of bias has been discussed in thousands of scientific articles and dozens of popular books, few of which even mention the issue of noise. This book is our attempt to redress the balance.”

Is the book successful in redressing this balance? Maybe, but I am not convinced. Here is my main issue with Noise: the book is trying to both argue that noise is rarely acknowledged and we know very little about noise (and, for that reason, we need new concepts to study noise) and summarise decades of research on noise (including research done by the authors themselves decades ago). The book is both trying to set a new research agenda with actionable insights (what Sunstein did with Nudge) and introduce the reader to decades of research (what Kahneman did with Thinking, Fast and Slow). You can’t have your cake and eat it too.

In reading the book you will learn about different types of noise, in particular system noise, level noise, pattern noise and occasion noise. Despite the graphical presentation of the sum of squares in various chapters, and especially in chapter 17 on the components of noise, it is difficult to follow the reasoning throughout the book. I am left with the feeling that some additional conceptual work is needed in order for the concepts to fully work. Alas, upon reading the book, the added value of the concepts remains limited. Also, at one place in the book, it is described that system noise can be broken into three components of noise (level, pattern, and occasion), whereas the authors later write “System noise can be broken down into level noise and pattern noise.” I believe the authors could have done a much better job by providing more conceptual clarity, for example with tables and additional useful examples to better demonstrate and discuss the usefulness and relevance of the concepts.

In particular, I would like to see the authors use existing concepts in the scientific literature instead of pretending that they are the first to come up with concepts to think about reliability. I can understand if the authors don’t want to rely on too many concepts, but an Appendix listing the relevant and related concepts would be great. Instead, bhe book begins by introducing the well-known distinction between bias (validity) and noise (reliability) without using mentioning reliability. (Surprisingly, the book is doing what it can not to mention the word reliability more than necessary.) Here the core distinction is also introduced: bias is systematic deviation and noise is random scatter.

The first chapter introduces a key theme of the book, namely that “wherever there is judgment, there is noise—and more of it than you think”. The authors then set out to understand noise in various topics, such as medicine, child custody decisions, forecasts, asylum decisions, personnel decisions, bail decisions, forensic science, and grant patents. However, the book is not interested in random scatter, but to explain systematic components to noise. That is, to actually focus more on biases than noise. Here is how chapter 1 begins:

Suppose that someone has been convicted of a crime—shoplifting, possession of heroin, assault, or armed robbery. What is the sentence likely to be?
The answer should not depend on the particular judge to whom the case happens to be assigned, on whether it is hot or cold outside, or on whether a local sports team won the day before.

This could have been the introduction to a lot of recent pop psychology books with one-word book titles. Again, the problem is that we are not really interested in noise here, but rather to show that there is less noise (by adding variables on judge characteristics, outdoor temperature, sport results, etc.). Accordingly, the book is more about showing that what we believe is noise is not actually noise. This is the noise paradox. Once you can explain random scatter, it is no longer random. Once we can explain noise, there is less noise. What the book is doing with a lot of examples is to say that there is less noise than we think there is (whereas the authors interpret this as there is more noise than we think). In sum, there are contradictory forces at play. On the one hand, the authors want to explain noise (i.e., say that we can understand and predict noise). On the other hand, the authors want to say that there is more noise than we expect (cf. “wherever there is judgment, there is noise—and more of it than you think”).

This focus on showing how “noise” matters, or how small and irrelevant factors matter, also brings some low-quality research into the book (see, for example, here and here). To be fair, the authors also rely on a lot of high-quality research, but they use low-quality research to make some extreme statements. Accordingly, the book is worst when it provides conclusions such as “If you are suffering political persecution in your home country and want asylum elsewhere, you should hope and maybe even pray that your hearing falls on a cool day.” Again, this has more to do with focusing on noise in data than providing actionable insights on how to deal with noise. This is also how you end up with coverage about the book such as “Why You Shouldn’t Buy Bitcoin When You’re Hungry“. In my view, such articles add more noise than signal to our understanding of human behaviour. And it is not doing a good job in redressing the balance.

The problem with the definition of noise in the book (or rather the lack hereof) is that every difference we can imagine can be understood as noise. Anywhere there is variation, there is noise. For example, because all countries in the world did not respond to the COVID-19 crisis in a similar manner, we can conclude that there is noise. Or, as the authors formulate it: “For another exercise in counterfactual thinking, consider how different countries and regions responded to the COVID-19 crisis. Even when the virus hit them roughly at the same time and in a similar manner, there were wide differences in responses. This variation provides clear evidence of noise in different countries’ decision making.” This is not the best way to think about noise, nor a good example of noise in decision making. (Also, apparently, there is so much noise in the world, yet here is the cognitive bias that makes you panic about a pandemic.)

It is old news that people do not respond to treatments (events, news, pandemics, Sundays, hunger, books, etc.) in a similar way and it is not surprising that all countries did not respond with the exact same policies to the COVID-19 pandemic. Even scientific studies differ in the results they find and we now have several meta-analytic techniques we can rely on to understand how effects vary over different persons, locations, treatments, and outcomes. However, if Noise is the first book you ever read, you will have the impression that very little work has been done to understand such differences in treatment effects.

The statement that noise can be any variation also means that the book is dealing with different topics that should not necessarily be put together if we are to reduce the noise in our thinking. The authors write that noise “is variability in judgments that should be identical.” This means that noise is both treatment heterogeneity (“If every respondent makes the same mistake, there is no noise.”) and measurement error (“Can we agree on an anchor case that will serve as a reference point on the scale?”). However, the authors do not convincingly show why we should use the same concept to consider both treatment heterogeneity and measurement error. On the contrary, it seems like the authors rely on a catch-all definition of noise solely for the purpose of bringing different points and arguments together within the same book. This also makes the book very difficult to review. There is simply too much going on.

Overall, I would have liked to see the authors focus more systematically on how little we can actually explain (instead of how much we can explain). For all the examples they provide throughout the book, to what extent are we actually able to predict behaviour? At some point the authors argue that correlations of about .2 are quite common in human affairs, but I find such interpretations too optimistic based on what we know from the replication crisis within psychology (and related fields). Speaking of correlations, I have also seen some discussions on how the book misunderstands how the absence of a correlation is not evidence of no causation (Rachael Meager made this point on Twitter, and see the discussion on Andrew Gelman’s blog where Daniel Kahneman also responds).

And speaking of “speaking of”. Each chapter ends with a few “Speaking of …” bullet points to capture the key points relevant to the chapter in question. However, these are often symptomatic for the fact that the genereal insights provided throughout the chapters are trivial at best and confusing at worst. Consider these randomly selected examples: “To fight noise, they first have to admit that it exists.”, “Wherever there is judgment, there is noise—and that includes reading fingerprints”, “Before we start discussing this decision, let’s designate a decision observer” and “We have kept good decision hygiene in this decision process; chances are the decision is as good as it can be”.

Ironically, the book is best in part 6 when the authors discuss the seven objections to reducing or eliminating noise: 1) it is not always cheap to reduce noise, 2) reducing noise can introduce bias, 3) eliminating noise can reduce the feeling of respect and dignity, 4) noise is a prerequisite for moral and political evolution, 5) noise makes it difficult for people to exploit a system, 6) noise introduce uncertainty that can be used in combination with biases to improve social outcomes, and 7) reducing noise might lead to less creativity. These are not original insights but there are some good discussions and it is great to see the book devote attention to the benefits of noise as well, especially to provide balance to the naive optimism provided throughout the book when it comes to noise reducing strategies such as algorithms.

Overall, if you care about noise (as you should), I will not recommend this book. Instead, if you do want a book with noise in the title, consider Nate Silver’s The Signal and the Noise (that also focuses on the work by Philip E. Tetlock, provides references to Moneyball, etc.) and Nassim Nicholas Taleb’s Fooled by Randomness. The latter provides a much better introduction to noise, uncertainty, and randomness. Recent books that deal with some of the same topics and ideas are Dark Data: Why What You Don’t Know Matters (I provided a few thoughts on the book here) and Radical Uncertainty: Decision-Making Beyond the Numbers.

Philip E. Tetlock describes Noise as “A masterful achievement and a landmark in the field of psychology” and Cass Sunstein said in an interview that: “Unlike bias, noise isn’t intuitive, which is why we think we’ve discovered a new continent”. This might very well be the case but why not acknowledge previous research on noise? If you are truly discovering a new continent, you need to provide a stronger case for this continent being new. For example, why not mention the work on noise traders in financial markets (inspired by Fischer Black, and also described in Thaler’s book Misbehaving). When not dealing with all of this research, Andrew Gelman is correct with his comment: “But then I realized that Sunstein kinda is like Columbus, in that he’s an ignorant guy who sails off to a faraway land, a country that’s already full of people, and then he goes back and declares he’s discovered the place.”

This is not to say that there isn’t a lot of new land to discover. There is a lot of potential in a book about noise. Noise is all around us and we are not good at dealing with noise. How, for example, do we use different strategies to deal with different types of noise? And what about when people do not agree with each other about what is noise and what is not? (We might call this “noise in noise”.) What is noise for some people can be a signal for other people. In opinion polls, there is a lot of noise that journalists use to create engaging narratives – but political scientists (at least those not yet turned pundits) see nothing but random error. (We used such noise to study how small differences within the margin of error affected the coverage of opinion polls.)

Noise is a book closer to being an appendix to Thinking, Fast and Slow and Nudge than an original piece of work that improves our understanding of human behaviour, and in particular the role of noise. Again, there is a lot of potential in providing a book-length examination of noise in human judgment and decision making. And while there are definitely good things to say about Noise, is a lost opportunity to provide such an examination. In sum, this is a fairly lengthy book with a low signal-to-noise ratio for people interested in the scientific study of noise/cognitive biases/measurement error/treatment heterogeneity/reliability.

There is a lot of noise in Noise.

Resources with research writing advice

I was going through a few resources with some good advice on writing research papers. Might be of interest to some of you:

How to write a great research paper (Simon Peyton Jones from Microsoft gives seven suggestions for how to improve your research papers)
Ten simple rules for structuring papers (Table 1 in the paper gives a good summary of the ten “rules”)
Writing Empirical Articles: Transparency, Reproducibility, Clarity, and Memorability (some good advice on how to write good science, e.g. increasing the transparency)
Writing a scientific paper, step by painful step (I’m not a fan of some of the suggestions, such as organising p-values, but overall a lot of good advice)
Robert’s Rules:Suggestions for Writing (motivational piece, e.g. “Write fast, in multiple drafts”)
10 Tips on How to Write Less Badly (especially relevant for people starting in grad school)
How to construct a Nature summary paragraph (good example on how to write a summary paragraph)
Publication, Publication (Gary King on how to structure a publishable paper)
Writing Tips for Ph. D. Students (a must read)
Common Expositional Problems in Students’ Papers and Theses (great set of advice — including some detailed advice on details)
Doing a Literature Review (recommended reading if you are doing a literature review)
Managing Your Research Pipeline (practical advice on how to structure multiple papers)
Mathematical Writing (+100 pages subject-specific advice on mathematical writing)
Three Templates for Introductions to Political Science Articles (great templates for introductions)
Writing Guide (Daniel Simons’ recommendations – including a good revision worksheet)
Of Publishable Quality: Ideas for Political Science Seminar Papers (great paper on how to think about ideas for research projects)
Rookie Mistakes: Preemptive Comments on Graduate Student Empirical Research Manuscripts (another great paper on rookie mistakes in empirical papers)
How to Read (and Understand) a Social Science Journal Article (focus is on reading an article but also relevant for writing)

Assorted links #2

31. Stanley Milgram and the uncertainty of evil
32. Electric Schlock: Did Stanley Milgram’s Famous Obedience Experiments Prove Anything?
33. LinkedIn’s Alternate Universe
34. Who Did J.K. Rowling Become?
35. The economics of Christmas trees
36. How To Understand Things
37. On The Exciting Subject Of Earwax And Unsupported Medical Arguments
38. The real David Attenborough
39. The UX of LEGO Interface Panels
40. 99 Good News Stories From 2020 You Probably Didn’t Hear About
41. Memos
42. The Observer Effect – Daniel Ek
43. The economics of vending machines

And here is a list with a few good Twitter threads:

44. Something I’ve learned while in law school is about the social construction of crime
45. In 40 tweets I will describe 40 powerful concepts for understanding the world
46. Here are 50 ideas that shape my worldview
47. Thread on creating your own CS degree online
48. Lessons from different fields
49. Bastion forts around the world
50. My top 12 favourite perceptual illusions
51. A meta-thread of some of my favourite Twitter threads

And some links to good video essays and clips on YouTube:

52. 4-2: The History of Super Mario Bros.’ Most Infamous Level
53. Exploring the Sonic Cocktail of Beastie Boys’ PAUL’S BOUTIQUE
54. Akira Kurosawa – Composing Movement
55. BOOKSTORES: How to Read More Books in the Golden Age of Content
56. How The Shawshank Redemption Humanizes Prisoners
57. Sexual Assault of Men Played for Laughs – Part 1 Male Perpetrators
58. Van Gogh’s Ugliest Masterpiece
59. The Art Of Sci-Fi Book Covers
60. This Is What a “Second-Person” Video Game Would Look Like


Previous posts: #1

Min Top 50 – Dansk rap

Med al den eksamenslæsning er det godt med et lille break, og her er det altid godt at lytte til lidt eminent dansk rap.

Følgende liste er min top 50 over de danske rap numre jeg i skrivende stund synes bedst om. Den spænder vidt, og der er klassikere som mange nok gerne så på listen, men som bare ikke rigtigt tiltaler mig (RBC-Engel for eksempel). Listen er i alfabetisk rækkefølge efter kunstner/gruppe.

  1. Ataf Khawaja – Sommerfugl
  2. Bikstok Røgsystem – Fabrik feat. Erik Clausen
  3. Bogstaveligt Talt – Træt
  4. Bumsestilen – Det Så’n Det R feat. MD
  5. Cab – Devisen
  6. Clemens – Den Anden Verden
  7. Clemens – Kun Få
  8. Clemens – Skaden
  9. Clemens – Den lyriske 9mm
  10. Clemens – Flakker Rundt & Snakker Ondt
  11. Clemens – En Blåtonet Gråzone
  12. Clemens – Fanget af fortiden
  13. Gramsespektrum – Bademestrene
  14. Ham Den Lange – B-menneske
  15. Hvid Sjokolade – Kronisk Fastelavn
  16. Jokeren – Det Ku’ Ha’ Været Dig
  17. Jyder Mæ Attityder – Både til gården og til gaden
  18. Jyder Mæ Attityder – Historien bag
  19. Jøden – Hamrer Løs
  20. Kasper Spez – Derfor Kontra
  21. Kasper Spez – Det’ så vigtigt
  22. L Ron Harald – Harald Te Enkebal
  23. L.O.C. – Snakker Ikk’ Med Nogen
  24. Lex L – Husk!
  25. Malk De Koijn – Kosmisk Kaos
  26. Malk De Koijn – P.I.G.E.
  27. Marwan – Min blok
  28. Organiseret Riminalitet – Holder døren
  29. Pede B – Jeg har en ven
  30. Pede B – Pis og papir feat. Es
  31. Pede B – Spisetid feat. Petter & Skurken
  32. Pede B – Tværtimod
  33. Per Vers – Black Power
  34. Per Vers – City Of Dreams
  35. Per Vers – Fyldosof
  36. Per Vers – Trøstesløs Café feat. Povl Dissing & Rune T. Kidde
  37. Rent Mel – Hold Dig Væk
  38. Revoltage – 24 timer
  39. Sund Fornuft – Hvorfor?
  40. Sund Fornuft – Syndebuk
  41. Sund Fornuft – Zen Midt I Centrum
  42. Suspekt – Dagen efter
  43. Suspekt – En vej
  44. Street Mass – Vores verden
  45. Strøm – Dét
  46. Torrpedorr – Lommen Fuld Af Guld
  47. Troo.L.S. & Orgi-E – Rolig-Rolig feat. Tue Track & Jooks
  48. UFO Yepha – Hvis jeg sad oppe på månen feat. Alex
  49. Vakili – Når Alt Kommer Til Alt
  50. Østkyst Hustlers – Hvordan Går Det?

Word.

Den gode borger

Der er nævneværdige værdier som jeg værdsætter hos mig selv. Den af de værdier jeg dog sætter mest pris på, er min åbenhed over for andres værdier, holdninger og synspunkter. Dette ser jeg netop som værende et udgangspunkt og forudsætning for, at kunne have nogle værdier der respekterer og tolererer andre mennesker. Den franske forfatter og samfundskritiker Voltaire er blandt andet citeret for at have sagt: ”Jeg er uenig i hvad De siger, men jeg vil forsvare til døden Deres ret til at sige det

I dette ytringsfrihedsvenlige udsagn, mener jeg, at der ligger en grundlæggende respekt for andre, en åbenhed der ligestiller ens værdier og standpunkter med andres, samt åbner op for en saglig debat og en erkendelse af, at der ikke findes definitive værdier, som ikke tåler at blive objekt for en kritisk vurdering. Dette står i en diametral modsætning til et konservativt livssyn, hvor man finder værdier bevaringsværdige ene og alene fordi, at ”sådan har det jo altid været”.

Et konservativt livssyn skildres ganske formidabelt i TV-serien Matador, hvor den troende Mads Skjern er en streng far som går meget op i, at børnene klarer sig godt i skolen og er vel opdraget. Hans konservative livssyn kommer blandt andet til udtryk da hans søn gerne vil være designer, men hvor Mads Skjern nægter med begrundelsen, at det er et job for kvinder. Et tilsvarende konservativt livssyn er næsten utænkeligt i dagens Danmark anno 2008. Dermed ikke sagt, at forælderen ikke skal bruge sin autoritet til at opdrage barnet, tværtimod.

Den gode forælder, den gode borger, tager forældreansvaret til sig, og gør sit bedste for at opdrage barnet til, at kunne blive en del af samfundet og ikke mindst demokratiet. Det kan lige frem være et problem hvis forælderen ikke påtager sig ansvaret for opdragelsen af sit eget barn, og overlader dette til et af samfundets institutioner eller Cartoons Networks, blot fordi at forældrene skal kunne passe deres job så længe de overhovedet kan. Grundmoralen er, at ideen med at have nogle værdier ikke er blindt at repræsentere nogle værdier for værdiernes egen skyld, men fordi man ser en dybere mening i at have disse værdier.

Ud fra dette mener jeg at kunne konkludere, at de værdier jeg er stolt af et besidde og sætter højt, er værdier som næstekærlighed og medmenneskelighed og demokratiske værdier som ytringsfrihed, forsamlingsfrihed, lighed for loven, ejendomsrettighed og så videre.

Det er altså for mig vigtigt at behandle andre som jeg selv vil behandles. Jeg tager så vidt muligt klart afstand til enhver brug af vold (det er vigtigt at gå ind og forholde sig kritisk til de værdier statens voldsmonopol bruges på at opretholde). Jeg går i det store hele ind for frihed under ansvar, og herunder ikke friheden til at køre 240 km/t, blandt andet på grund af den fare man udsætter andre for. Enhver handling man foretager sig som samfundsborger, bør ske med respekten for andre borgeres frihed og (rets)sikkerhed in mente.

Den gode borger er altså en borger der accepterer de demokratiske grundprincipper og netop, om muligt, engagerer sig i disse. Den gode borger er opdraget med en grundlæggende respekt for andre mennesker samt viden om hvordan man opnår indflydelse og bliver hørt i det demokratiske system. Som en god samfundsborger tager man del i den politiske debat, deltager i foreningsarbejde og i demokratiske valg. Den gode borger reducerer ikke sig selv til at være den gode forbruger hvor værdier købes på flaske, men accepterer, at forbrug ikke nødvendigvis er vejen frem til værdier og mening i tilværelsen.

Den gode borger har ikke nødvendigvis et ambitionsniveau om at være den bedste i skolen, men en lyst til at udnytte sit potentiale og opnå noget i tilværelsen, der giver mening, og skaber lykke for den enkelte. Den gode borger er altså et stærkt individ, men ikke egoist.

Fordi at man udelukkende scorer tolvtaller i skolen og ender med et højtlønnet job, er det ikke ensbetydende med, at man opnår at leve et liv man føler sig tilfreds med. Der er ingen tvivl om, at man er en god borger for staten, hvis man tager en længere videregående uddannelse og får en kandidat-uddannelse, men jeg mener nu engang, at en god borger er en borger, der kæmper for at opnå mest mulig lykke i sit liv, og det er uafhængigt af om ens passion er at tjene en million, stifte familie, blive pædagog eller lave mad. Hertil kommer så at det kun er en fordel for almenvellet, hvis man har overskuddet til at tage del i samfundet og være med til at gøre rammerne for andre borgeres selvrealisering bedre.

Den gode borger gør hvad han/hun kan for at opnå livskvalitet, ud fra de rammer tilværelsen har givet ham/hende, og arbejder for at gøre andres rammer for at opnå livskvalitet bedre. Det handler ganske enkelt om at udnytte det potentiale man besidder, eller som filosoffen John Engelbrecht har formuleret det:

”Gør din indsats. Brug din chance. Livet er et spil. Som beror på balance. Spil dine kort. Med kløgt og elegance. Så din livskvalitet vokser. Og øges i avance. Det fortælles, at en citron er en grapefrugt, der fik chancen – og tog den.”.

Den gode borger skaber altså selv mening i tilværelsen, men gør sig ej heller til smagsdommer for, hvad andre føler giver mening i livet, men arbejder aktivt for at skabe rammerne til, at ens medmennesker kan leve en tilværelse der giver mulighed for selvrealisering og selv blive en god borger.

Hvad enten man vil det eller ej, lever vi i en multikulturel og globaliseret verden, og der er da også uden tvivl forskellige værdier der vil støde på hinanden og mødes. Dette ser jeg dog kun som en positiv ting, netop at få anledning til at give egne og andres værdier modspil i et forsøg på at stræbe efter værdier der giver en mening i tilværelsen, og dermed gør en til en god borger.

Jeg må i den forbindelse også stille mig uforstående over for den overbevisning, at vi styrker egne værdier ved at håne andres. Dette kan lyde meget utopisk, men som udgangspunkt må man stræbe efter at der er plads til alle samt disses værdier. Er man for eksempel en god borger, hvis man ikke accepterer andres værdier og blot bruger egne værdier som en illusion til at stirre sig blind på? Det mener jeg ikke er tilfældet. Dette fordi at en værdi ikke blot handler om at stå inde for noget, men også om at distancere sig fra noget andet, men hvis man konsekvent nægter at tage stilling til, hvad man tager afstand fra, besidder man ikke en værdi jeg nødvendigvis finder bevaringsværdig.

Jeg er tilhænger af menneskets ret til at ytre sin holdning i det offentlige, fordi jeg ved, at alternativet historisk set har vist sig at føre til politisk ensretning og minoritetsforfølgelse.
Den gode borger for mig er altså, for at opsummere, en borger der accepterer de demokratiske grundprincipper og besidder en respekt for andre mennesker, samt de værdier der giver en substans og ikke hindrer andre i, at have deres værdier og stræbe efter mest mulig livskvalitet.