Saturday, January 10, 2015

Perspectives on Human Cooperation Workshop

Spent all of yesterday at the anthropology department of UCL for the second edition of a workshop on Perspectives on Human Cooperation. It was a great conference. Lots of interesting talks by people from various academic disciplines - anthropologists, economists, psychologists, philosophers, behavioural biologists etc. - but also from people out there in the real world; Rory Sutherland from Ogilvy opened and Michael Sanders from the Behavioural Insights Team did the last talk. From my experimental economic perspective my favourite talks were the following:

University of Bristol's Sarah Smith analysed data from an online fundraising site - JustGiving I think it was. Here people can announce to do stuff for charity and ask friends and family to donate money. This all happens in public so people can react to or be influenced by what's happened before. Smith showed firstly that one large donation - defined as more than 2x the average - has a positive effect on later donations. Contributions after will be, on average, higher than the contributions before the large donation. She also introduced an interesting evolutionary psychological twist by looking at gender and attractiveness. This positive effect of one large donation is especially strong for male donors making donations to attractive female fundraisers.

Ruth Mace talked about some field experiments in Northern Ireland investigating in- and out-group cooperation. They asked people to donate to either protestant or catholic or neutral charities. They not only looked at whether people gave more to their 'own' group but also at the influence of the level of the sectarian tension. The higher this 'threat index' the lower the contributions to the out-group but it didn't have an effect on contributions to the in-group.

Daniel Richardson from UCL gave a fun talk about his mass participation research. One of the experiments he described was a large scale public goods game. On the basis of their behaviour they could identify four types of players: warm glow altruists (who contribute always), free riders (who contribute never), tit-for-tat-ers (who do what the rest of the group did last round) and foresight-ers (who do the opposite of what the rest of the group did last round). The majority seems to be tit-for-tat player but the foresight people are pretty important for the rest of the group because once cooperation starts to decrease after a few rounds they encourage cooperation by going against the trend and inspire the tit-for-tat-players (and, once cooperation has increased again, do pretty well financially by free-riding themselves).

Nichola Raihani's talk about third-party punishers who observe (and reward/punish the players in) some exchange and who are in turn observed by another level of bystanders who can reward their behaviour was also pretty interesting but I will have to read to actual paper first to know what was going on exactly because I missed/forgot some of the details.

Tuesday, October 15, 2013

Why Things Cost $19.95 (Reprise)

A couple of years ago I blogged about research that tried to come up with an explanation for why many prices end in '99' or '95' rather than being a round number. The study found that participants guessed the wholesale price of a plasmascreen tv to be lower if the retail price was $5000 than when it was $4.888 or $5.012. Suggesting that round numbers trigger a wider frame of reference than more exact numbers. I was reminded of this research after I read about a new study that looks at the difference between round and non-round offers in negotiation situations. This study finds that second-movers make greater counteroffer adjustments to round than to precise offers. The authors argue - and provide some evidence for this - that 'precise numerical expressions imply a greater level of knowledge than round expressions and are therefore assumed by recipients to be more informative of the true value of the good being negotiated'.

Thursday, December 20, 2012

Diederik Stapel and Benford's Law

When the Diederik Stapel scandal was first in the news last year, I thought it might be interesting to play around with his data and Benford's Law. Benford's Law is a statistical artefact that describes how the frequencies of the first digits in all sorts of large, natural datasets are not, as you'd perhaps expect, distributed evenly. There are not just as many numbers starting with '1' as there are numbers starting with '6' or '9'. In fact, the distribution follows a particular pattern with 1 being the most often observed first digit (around 30%) and 9 the least (around 5%). This finding is confirmed in all kinds of data sets and the phenomenon is occasionally used to check for fraud. The idea being that data that is made up or manipulated won't have the right distribution (Here's an example in the Economist, earlier this week).

I had seen a couple of examples of the application of Benford's Law to spot scientific fraud and I thought it could be interesting to use the Stapel case to see how it would work in experimental social science. I didn't spend too much time on it. I took Stapel's CV and started tracking down his most recent publications (at that point none of which were retracted yet). Of his publications in 2011 I managed to get hold of the following 10 via the university library:

1. Stapel, D.A., & Lindenberg, S. (2011). Coping with chaos: How disordered contexts promote stereotyping and discrimination. Science, 332,251-253.
2. Lammers, J., Stoker, J.I., Jordan, J., Pollmann, M.M.H., & Stapel, D.A. (2011). Power increases infidelity among men and women. Psychological Science, 22, 1191-1197.
3. Stapel, D.A., & Van der Linde, L.A.J.G. (2011). What drives self-affirmation effects?: On the importance of differentiating value affirmation and attribute affirmation. Journal of Personality and Social Psychology, 101, 34-45.
4. Johnson, C.S., & Stapel, D.A. (2011). Happiness as alchemy: Positive mood and responses to social comparisons. Motivation and Emotion, 35, 165-180.
5. Stapel, D.A., & Noordewier, M.K. (2011). The mental roots of system justification: System threat, need for structure, and stereotyping. Social Cognition, 29, 238-254.
6. Van Doorn, J., & Stapel D.A. (2011). When and How Beauty Sells: Priming, Conditioning, and Persuasion Processes, Journal of Consumer Research, published online June 1, 2011.
7. Lammers, J., & Stapel, D.A. (2011) Racist biases in legal decisions are reduced by a justice focus. European Journal of Social Psychology, 41, 375-387.
8. Lindenberg, S.M., Joly, J.F., & Stapel, D.A. (2011). The norm-activating power of celebrity: The dynamics of success and influence. Social Psychology Quarterly, 74, 98-120.
9. Johnson, C.S., & Stapel, D.A. (2011). Reflection vs. Self-reflection: Sources of self-esteem boost determine behavioral outcomes. Social Psychology, 42, 144-151. 72
10. Lammers, J., & Stapel, D.A. (2011). Power increases dehumanization. Group Processes & Intergroup Relations, 14, 113-126.

My way of data collection was pretty crude. I took the results section(s) of these papers (and only the results sections) and simply scored every number I encountered. The only distinction I made is that I didn't count P-values (because they were often reported inexactly, p < 0.05 etc) and number of participants (can't really remember why I chose not to include these...). Things I did count included F statistics, t values, means, SD's, path coefficients, correlation coefficients, Cronbach's alpha's etc.. I tried to avoid double counting certain numbers - numbers that were presented in a table but also referred to in the text - but I worked pretty quickly and didn't actually read the texts so I probably overlooked many instances. I ended up with a data set of 1107 numbers, had Excel extract their first digits and made a bar graph with their frequencies. It certainly didn't look like the distribution as predicted by Benford's Law but I didn't quite know what to make of it. One thing I had noticed collecting the data for instance was that Stapel used a lot of 7-point Likert scales in his 'research'. It doesn't seem unlikely that that will influence the kind of first digits used. I wasn't sure if a data set based on similar but non-fraudulent papers would actually follow Benford's Law. I thought about collection data on other, presumably non-fraudulent research but regular work got in the way and the project ended up in a drawer.

It stayed there until about a month ago when the committees installed by the three universities where Stapel worked during his career - Amsterdam, Groningen and Tilburg - presented the results of their investigation into his work. Not all of Stapel's articles were based on fabricated data. The rapport includes handy lists of all of Stapel's publications with an indication of whether the committee had 'established fraud' or not. Out of the 10 articles published in 2011 that I had collected 7 were deemed to be fraudulent (1,3,4,5,6,7,8) and the remaining 3 were apparently not (2,9 and 10). So now I could make the comparison between Stapel's real and fake data and check the distribution of the first digits in both data sets (with 186 and 921 numbers respectively).

That worked pretty well, I thought. I'm especially surprised with how well the distribution of the first digits from the non-fraudulent papers seems to follow Benford's Law (as said, only 186 digits, from 3 papers). There appear to be more 5's than predicted, but maybe that's a consequence of using all those 7-point Likert scales. The first digits from the fraudulent papers clearly don't follow Benford's Law, seemingly confirming the committees' conclusion that they were made up. This is, of course, just a very simple analysis. There are more than a few possible catches. Perhaps the data of a particular kind of research were easier to fudge than other kind of research and all the graph shows is the difference between research with, say, lots of Likert scales and other research. And I doubt there is a big future for Benford's Law in spotting scientific fraud. As soon as potential fraudsters know their results will be tested this way they can simply make up their numbers so that they fit. But, for now, I thought this was a pretty neat example of the application of Benford's Law in the experimental social sciences.

Thursday, August 06, 2009

Six thousand chess players took part in a beauty contest

Researchers from the University of Kassel ran a Beauty Contest experiment among visitors of the ChessBase website. Compared to previous versions of the game run on the internet the chess players don't seem particularly smarter than other types of players (although drawing any rockhard conclusions from comparing between completely different experiments is a bit pointless). The most interesting part of the results is that better chess players - as identified by a higher ELO rating - pick lower, better numbers, although the effect is not particularly big and the grandmasters (n=28) who participated didn't seem to do better than the rest at all.

Sunday, June 07, 2009

Revenge and the people who seek it

[T]he punishers reported feeling worse than the non-punishers, but predicted they would have felt even worse had they not been given the opportunity to punish. The non-punishers said they thought they would feel better if they'd had that opportunity for revenge—even though the survey identified them as the happier group.
Monitor on Psychology reviews some recent(ish) studies on revenge.

Tuesday, May 26, 2009

Secret of Googlenomics

"All of a sudden we realized we were in the auction business."
Wired on Hal Varian's work as Google's Chief Economist.

Friday, May 22, 2009