Cataloging a year of blogging: from behavior to society and mind
January 10, 2014 1 Comment
For psychologists, memory and learning are intimately intertwined. In fact, during the years of behaviorism in the early 20th century, the unobservable process of memory was completely replaced in the technical lexicon by learning (Miller, 2003). I want to take this post as an opportunity to remember the year that’s past, and the 83 articles that were published here during it. In the first theme, a few days ago, I discussed traditional applications of evolutionary game theory. However, I can’t confine myself to traditional applications and find it important to push the envelope a little. Today’s theme is concerned with completing the rejection of behaviorism, not only in psychology but also evolutionary biology: expanding from behavior to society and mind. In the process, we can start to understand how internal representations, learning, and culture shape evolution. The articles listed here were primarily concerned with extending existing methods to new problems, and I’ve saved the most radical developments for the next theme of envisioning the algorithmic world.
Representations and rationality for replicators
At the time that biology was finding the modern evolutionary synthesis (roughly 1935-1950), behaviorism was the dominant view in psychology, although it was undergoing some relaxation as Stevens (1935a,b) and Hull (1943) introduced ideas from Brigman’s operationalism. The movement away from behaviorism continued in psychology, culminating in the cognitive revolution of the 50s. Unfortunately, biology has largely remained trapped in behaviorism to this day, and evolutionary game theory (EGT) has inherited this orthodoxy.
Since I am interested in applying EGT to psychological and antrhopological questions, I thought it prudent to see how we can get around a purely behaviorist framework. Back in 2012, Marcel Montrey, Thomas Shultz, and I started looking into how to reason about both objective behavior and subjective experience within an EGT models. Through out the last year, we slowly worked on developing this project and my second reason for visiting Florida in July was to present our preliminary work at Swarmfest 2013 in Orlando. In an effort of open science, we diligently tracked our progress on the blog:
- Habitual selfish agents and rationality by Marcel Montrey
- Rationality for Bayesian agents
- Extra, special need for social connections by Thomas Shultz
- Quasi-magical thinking and the public good by Marcel Montrey
- Quasi-magical thinking and superrationality for Bayesian agents
- Quasi-delusions and inequality aversion
- Evolving useful delusions to promote cooperation
- Cooperation through useful delusions: quasi-magical thinking and subjective utility
This project has been an exciting test bed for the pair programming coding technique that Wei Lu and Kyler Brown introduced me to back in 2012, and Marcel and I are excited to publicly share our code on github as we submit this paper. The paper is almost finished, with mostly small things to pretty up like the figure I was working on today.
However, this will not be the end for the whole project. Everything is still treated in the abstract and the bridging will be incomplete without neural considerations. Over the year, to get an introduction to the relevant aspects of neuroscience, I have become a dedicated reader of Adam J Calhoun’s neuroecology blog.
This category had a corpus of around 10.3 thousand words long and garnered around 4.6 thousand views.
Feedback between finance & economics and ecology & evolution
Biology is not the only field that shaped EGT, economics was an important contributor and over the last year I turned to the dismal science for inspiration. Although there are subfields like evolutionary economics that bridge the two sciences, due to the influence of Yunjun Yang and Kate Zen I looked for insight in finance as well. This was often not EGT specific, but connected more broadly to ecology and evolution. The main relation to EGT was looking at the theme of trade-offs between individual and social utility:
- Mathematical models in finance and ecology
- Individual versus systemic risk in asset allocation by Yunjun Yang
- Evolutionary economics and game theory
- Mathematics in finance and hiding lies in complexity
- Liquidity hoarding and systemic failure in the ecology of banks
- Evolution as a risk-averse investor
A surprising side-effect was that looking at models in finance and reading the Dynamic Ecology blog birthed as interest in ecology, an interest I hope to pursue more this year. Jason Collin’s Evolving Economics blog has also served as a valuable resource and source of discussion. I also did a bit to popularized this theme by writing for TruthIsCool; it was an interesting experiment in being a professional writer, but I’ll stick to my day job.
This category had a corpus of around 8.1 thousand words long and garnered around 5.3 thousand views.
Learning, intelligence, and the social brain
During the half-century long eclipse of Darwinism that preceded the modern synthesis, biologists actively explored alternative to natural selection such as Lamarkism. The double backlash of the demise of Lamarckism and the popularity of behaviorism, marginalized learning in evolution with few remembering the importance of the Baldwin (1886,1902) effect as a means of non-heritable learning affecting evolution. As the cognitive revolution began in psychology, Simpson (1953) returned interest to the feedback between evolution and learning and a small community of scholars have investigated its consequences since.
Hence, learning plays a central role in both biology and psychology. However, in both settings, learning is nearly impossible to discuss without looks at its social context. This line of inquiry forced me to concentrate on the interaction of evolution, learning, society and culture. Throughout the year I worked with Keven Poulin on understanding the social brain hypothesis, underlying questions of trust and trustworthiness, and a more general considerations of conditional behavior that connected us back to my previous studies of ethnocentrism. I also tried to tie social learning to individual-versus-group interests that we usually see in social dilemmas and that Eric Bolo and I had worked on (as mentioned in the previous theme).
- Games, culture, and the Turing test (Part I)
- Games, culture, and the Turing test (Part II)
- Interdisciplinitis: Do entropic forces cause adaptive behavior?
- Social learning dilemma
- Learning and evolution are different dynamics
- Cooperation and the evolution of intelligence by Keven Poulin
- Conditional cooperation and emotional profiles by Keven Poulin
- Replicator dynamics of cooperation and deception by Keven Poulin
- Hunger Games themed semi-iterated prisoner’s dilemma tournament
- Bounded rationality: systematic mistakes and conflicting agents of mind
- Baldwin effect and overcoming the rationality fetish
- Enriching evolutionary games with trust and trustworthiness by Thomas Shultz
The Simpson-Baldwin effect is extremely interesting to me, and it is one of the problems where I expect the algorithmic biology approach I have been developing over the last year (to be featured in the next catalog) to be particularly useful. As such, I have several projects in preliminary stages that will blossom throughout 2014. I owe a great debt to Adam Benton’s EvoAnth blog for my understanding and interest in the social and cultural aspects of learning and evolution. Adam, Keven, and I have bounced around a few ideas for building on some ideas in Adam’s undergraduate thesis that we will hopefully have a chance to continue.
Of course, life isn’t all research projects and seriousness. I also had fun writing for TruthIsCool about Dawkins’ wacky talk on one of the central concepts of cultural evolution — memes:
This category had a corpus of around 18.2 thousand words long and garnered around 19.5 thousand views.
This is the second theme of a three part summary of the 83 articles that came out in 2013 on this blog. The first theme was established applications of evolutionary game theory and the next theme is envisioning the algorithmic world.
Baldwin, J.M. (1886). A new factor in evolution. Amer. Nat., 30: 441-451, 536-553.
Baldwin, J.M. (1902). Development and evolution. Macmillan, New York.
Hull, C. L. (1943). Principles of behavior: An introduction to behavior theory.
Miller, G. A. (2003). The cognitive revolution: a historical perspective. Trends in cognitive sciences, 7(3): 141-144.
Nelson, R.R., & Winter, S.G. (1985). An evolutionary theory of economic change. Harvard University Press.
Simpson, G.G. (1953). The Baldwin effect. Evolution, 7 (2), 110-117 DOI: 10.2307/2405746
Stevens, S. S. (1935a). The operational basis of psychology. The American Journal of Psychology, 47(2), 323-330.
Stevens, S. S. (1935b). The operational definition of psychological concepts. Psychological Review, 42(6), 517.