I am working as the lab manager of the Decision Lab at the Max Planck Institute for Research on Collective Goods and I am currently a Ph.D. candidate of Economics and Management at the University of Trento and Cognitive and Experimental Economics Laboratory (CEEL). I got my B.A. in Economics from Istanbul University and M.Sc. in Economics from Istanbul Bilgi University. I worked previously as the lab manager at Bilgi Economics Lab of Istanbul (BELIS).
My Ph.D. dissertation, supervised by Luciano Andreozzi and Matteo Ploner , aims to investigate how social preferences are shaped by evolutionary processes.

As a general theme, my research is focused on the topic of cooperation, specifically on reciprocity and conditional cooperation. One particular application that I am interested in (and I find fascinating) is social production goods -such as open-source software, wikis, public guides and so on- which a large number unrelated individuals cooperate to produce those, oftentimes for public benefit.

For my research, I use methods from microeconomics, game theory, behavioral and experimental economics, agent-based modelling and social choice theory fields.

I am also interested in programming, especially related to experimental social sciences and data analysis. I have been using and teaching z-Tree for a while (check out my cheat sheet if you'd like). Recently we developed zBrac,a tool to help translation and text editing of z-Tree files. You can find my CV here My public profiles are like following:
Github @seyhunsaral
Twitter @seyhunsaral


Ongoing Research

Evolution of Conditional Strategies in Asynchronous Prisoners Dilemma (with L. Andreozzi and M.Ploner)


We aim to investigate the evolution of simple conditional response strategies in a cooperative environment. We start by defining properties of response function types in a general theoretical framework. Then we analyze the evolutionary stability of response strategies in a mixed population by using simulations based on an extended repeated prisoners dilemma game. Aligned with game theoretical literature our results confirm that the pure selfish type is likely to dominate the population when the discount factor is sufficiently low while two distinct conditional cooperator types are most likely types to survive when repetition probability is sufficiently high. However, our result show that, being a counter intuitive strategy, a particular hump-shaped response strategy is the most successful conditional strategy in the population for some mediate values of discount factor. Our result provide an intuition to explain the consistent existence of hump shaped responses obvesred in several experimental studies.

The Stability of Reciprocity: Egoism trumps Reciprocal Cooperation in Social Dilemmas (with L. Andreozzi and M.Ploner)


An often replicated result in the literature on social dilemmas is that a large share of the subjects reveal conditionally cooperative preferences. Cooperation generated by this type of preferences is notoriously unstable, as individuals reduce their contributions to the public good in reaction to other subjects' free riding. This has lead to the widely shared conclusion that the cooperation observed in experiments (and its collapse) is mostly driven by imperfect reciprocity. In this study, we explore the possibility that reciprocally cooperative preferences may themselves be unstable. We do so by observing the evolution of subjects' preferences in a standard, anonymously repeated social dilemma. Our unsettling result is that, in the course of the experiment, a significant fraction of reciprocally cooperative subjects become egoistic, while the opposite transition is never observed. The non-selfish preferences that appear to be more stable are those most easily attributed to confusion. We are thus driven to the conclusion that egoism is more resistant to exposure to social dilemmas than reciprocity.

Reciprocity in Dictator Games: an Experimental Investigation (with L. Andreozzi and M.Faillo)


The Dictator Game has been for a long time considered the sharpest tool to isolate pure altruism from reciprocity. However several criticisms were raised to this approach. First, there is the issue of their external validity: Giving money to randomly chosen strangers seems to be absent among the repertoire of human behavior. Second, experimental evidence has consistently shown that innocuous manipulations of the experimental framework change dramatically subjects’ decision, in ways that are incompatible with rational choice. We argue that positive giving in Dictator Games can be best understood as a form of reciprocal altruism: A subject’s decision to donate when in the Dictator’s position might stem from his implicit belief that the recipient would have donated as well if the roles were reversed. To test this hypothesis, we conducted three experiments: (i) Dictator game, (ii) A reframed dictator game known as “Bully Game” (Krupka & Weber, 2008), and (iii) Dictator Game in which subjects are matched with members of Achioli ethnic group located in Northern Uganda. In each pair, a subject is assigned to the role of dictator and the other to the role of responder. Before that, we used strategy method to collect subjects’ unconditional transfer decisions and transfer decisions conditioned to their opponents’ unconditional transfer decisions. Our main result rejects unconditional altruism hypothesis: In all treatments, majority of the subjects are conditional altruists although, on average, transfers in Achioli treatment are higher. We also confirm that simple framing can manipulate behavior in the dictator game considerably.


zBrac: A multilanguage tool for z-Tree (with Anna Schröter)

We developed a tool to facilitate translation of z-Tree treatment files. For more information, please visit the project repository on GitHub


For your requests, comments, collaboration proposals or questions: seyhunsaral [at] gmail.com. You can also contact me via Twitter .

Friendly note: Although I try, I often cannot respond all the e-mails I receive about general coding issues due to my time constraints. Thank you for your understanding.