I am currently a Ph.D. candidate of Economics and Management at the University of Trento and Cognitive and Experimental Economics Laboratory (CEEL) and I am working as the lab manager of the Decision Lab at the Max Planck Institute for Research on Collective Goods.

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 reciprocity and conditional cooperation using experimental and computational methods.

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 software development, especially related to experimental social sciences and data analysis.
You can find my CV here .
My public profiles are like following:

Github @seyhunsaral
Twitter @seyhunsaral


Ongoing Research

Evolution of Conditional Cooperation : An agent-based model*

Status: In progress
Show abstract | GitHub Repo
Reciprocal altruism is considered to be a strong mechanism that can promote cooperation, if individuals are likely to interact. Experimental evidence shows that most of the people have reciprocal tendencies when they interact in social dilemma experiments, although the members of the population as a whole seem to be heterogeneous in terms of their conditional strategies. In addition to that, those who employ conditionally cooperative strategies also seem to have a certain degree of selfish bias; they tend to give less than what others give do. Cooperation by the individuals who employ such preferences is bound to collapse as the level of cooperation tends to decrease over time.

In this study, we investigate conditional types and their evolution in an iterated Prisoner's Dilemma , comparing different continuation probabilities, by using a computational model. In our setting, agents are characterized by their responses to each level of cooperation in a linearly extended Prisoner's Dilemma. By using repeated simulations, we estimate the likelihood of cooperation and the conditional strategies that are likely to succeed.

Our results show that, when the continuation probability is sufficiently large, full cooperation is achieved. In this case, the most successful strategies are the ones who employ an all-or-none type of conditional cooperation, followed by perfect conditional cooperators. In the intermediate levels of continuation probability, however, hump-shaped contributor types are the ones that are most likely to exist, followed by imperfect conditional cooperators. Those agents cooperate in a medium level of cooperation within themselves and each other.

Our results provide an explanation for the commonly observed hump-shaped strategy and imperfect conditional cooperators in experiments. Furthermore, a potential implication of our results is that the heterogeneity of conditional strategies might stem from the diverse interaction frequencies among real-world interactions.

The Stability of Conditional Cooperation*

(with L. Andreozzi and M.Ploner)
Status: Submitted.
Working Paper | Show abstract | GitHub Repo
An often-replicated result in the literature on social dilemmas is that a large share of 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 led to the widely-shared conclusion that 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 an 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 reverse is rarely 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.

Presumptive Reciprocity in Dictator Games

(with L. Andreozzi and M.Faillo)
Status: In progress
Show abstract

The Dictator Game was initially introduced as a test for pure, non-reciprocal altruism. The large experimental evidence in the early literature revealed that subjects often share a fraction of their endowment; in addition, a considerable number of them even choose an equal split. In the years that followed, much criticism was levelled against these results. Some claimed that the evidence collected in the lab has little external validity. The kind of generosity we observe in the Dictator Game does not seem to be compatible with the giving patterns we observe in real life. Outside of the lab, donations to strangers usually target carefully chosen individuals or groups in need of help. Others observed that giving in the Dictator Game can be easily manipulated with trivial changes in the framework in which the game is presented. This originated a theoretical literature that tries to explain dictator giving in terms of other motives besides altruism, such as self-image and reputation.

In this essay, we take a more traditional stance. We argue that part of giving in the dictator game may in fact be explained in terms of altruism. However, it is a type of reciprocal altruism which is based on the presumption that the recipient would have behaved altruistically as well, if the roles were reversed. To do so, we use the strategy method to elicit subjects' preferences in a Dictator Game with randomly assigned roles. We ask subjects to choose a level of giving, conditional on possible levels of giving of the other player. We compare our results from two treatments: (i) when the recipient is a peer in the laboratory; (ii) when the recipient is a member of a low socioeconomic group. The presumptive reciprocity hypothesis predicts that when subjects play against their peers they should reveal conditionally altruistic preferences. Instead, they should be more likely to be unconditionally altruistic when playing against a subject in need. We find that these intuitive predictions are only partially borne out by the data. Whether giving is directed to a person with similar socioeconomic status, or it is directed to a person with low socioeconomic status, most subjects reveal conditionally altruistic preferences. Unconditional altruism seems to be rare for both treatments. However unconditional altruists thansfer a significantly higher amount to the members of the group with a low socioeconomic status.

Our results suggest that a large part of the altruistic behavior observed in dictator game can be explained by presumptive reciprocal altruism, while unconditional altruism has a relatively limited role even in those cases in which it should.

* Corresponding Author


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

GitHub Repo

We developed a tool to facilitate translation of z-Tree treatment files. The software is a Libre/Open-Source and licenced under GNU General Public License v3.0. To download the software please visit the the GitHub repository of the project .

The paper on zBrac is published as: Saral, A. S., & Schröter, A. M. (2019). zBrac—A multilanguage tool for z-Tree. Journal of Behavioral and Experimental Finance, 23, 59-63.

Please feel free to get in touch with me or to create an issue on GitHub for your suggestions and comments.


For your requests, comments, collaboration proposals or questions: saral [at] posteo.de. 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.