Christian Hilbe

For further information on the project please refer to http://web.evolbio.mpg.de/social-behaviour/ or contact Christian <hilbe@evolbio.mpg.de> after having read the below stated literature.

The doctoral researcher will be a member of the Max Planck Research Group “Dynamics of Social Behavior”. We work in an international and interdisciplinary team, with members having backgrounds in applied mathematics, physics, psychology, and economics. The group has a strong international network, and it explores questions related to cooperation, learning, and to (human) decision-making more generally.

To apply, please get in touch with Christian <hilbe@evolbio.mpg.de>. Your email should contain a CV and the names of two referees as well as a description of why you are interested in the position, and why you might be a good fit.
Email subject line project 1: “Doctorate Dynamics of Social Behavior: Experiments”
Email subject line project 2: “Doctorate Dynamics of Social Behavior: Theory”
 

1. Experimental approaches to human cooperation, coordination, and competition

People routinely cooperate: they donate to charities, they help strangers, and they form teams to solve problems that are hard to tackle individually. In each case, they pay some cost (e.g., time or money) in order to benefit someone else. In the past years, there has been a strong interdisciplinary effort to better understand these acts of cooperation, and how they depend on individual motives and reputational concerns.

One angle to address these questions is by using controlled behavioral experiments. For these experiments, strategic interactions are translated into simple games. These games are then tested with human participants, either in an experimental laboratory, or using online experimental platforms.

We look for a doctoral researcher who will use such experiments to explore strategic cooperation, coordination, and competition. For the specific project, there are several possible directions. One direction is to explore how people cooperate in changing environments. For example, interesting questions arise when cooperation costs can fluctuate in time, but participants only have incomplete information about their partner’s current costs. Another direction is to explore how recent developments in artificial intelligence affect cooperation. Here, the applicant could study how people use AI to make cooperative decisions, or how they interact in strategic interactions with AI agents. The eventual project can be tailored to the applicants’ interests and qualifications.

Applicants should have a master’s degree in biology, psychology, economics, computer science, or a related field. Ideally, they have some experience in designing and programming behavioral experiments (e.g., with oTree or a similar software). In addition, they should have some knowledge of statistics and statistical software (e.g., R or STATA). Any training pertaining to strategic decision-making is a plus.

References for further reading:
(-) Hauser, Hilbe, Chatterjee, Nowak (2018) “Social dilemmas among unequals”, Nature, https://doi.org/10.1038/s41586-019-1488-5

(-) Rossetti, Hilbe, Hauser (2022) “(Mis)perceiving cooperativeness”, Current Opinion in Psychology, https://doi.org/10.1016/j.copsyc.2021.06.020

2. Mathematical models of cooperation in changing environments

Social dilemmas are at the core of many social interactions. They describe situations in which groups of individuals fare best if everyone cooperates, yet each group member individually prefers to defect. Examples of social dilemmas range from favors among friends all the way to contributions of countries to avoid climate change. Mathematical models of social dilemmas help us understand what makes people help each other, and which mechanisms can further promote cooperation.

Many existing models assume that interactions take place in a constant environment, and that the incentives to cooperate are always the same. Recently, researchers have challenged this assumption by considering cooperation in so-called ‘stochastic games’. In a stochastic game, a group’s (social or ecological) environment can change over time, depending on the group’s previous interactions. For example, if no one cooperates, the group’s environment may deteriorate, which affects individual incentives to cooperate. With this framework, one can address new exciting research questions: Which environmental feedbacks are particularly favorable to the evolution of cooperation? Which kinds of strategies would individuals learn to use in changing environments? These are the kinds of questions that the doctoral researcher will tackle.

Applicants should have a master’s degree in mathematics, biology, economics, physics, computer science, or another related field. They should have a solid background in mathematical modeling; ideally, they are familiar with the theory of Markov chains and stochastic processes. Good quantitative skills and interest in programming (e.g., Matlab or Python) will be important. Any training pertaining to social behavior is a plus.

References for further reading:
(-) Hilbe, Simsa, Chatterjee, Nowak (2018) “Evolution of cooperation in stochastic games”, Nature, https://doi.org/10.1038/s41586-018-0277-x

(-) Kleshnina, Hilbe, Simsa, Chatterjee, Nowak (2023) “The effect of environmental information on evolution of cooperation in stochastic games”, Nature Communications, https://doi.org/10.1038/s41467-023-39625-9