Maria is full professor at the University of Bologna, where she carries out her research in the field of experimental and behavioral economics. Her main research interests are focused on the study of cooperation in repeated social dilemmas and learning dynamics, with applications within game theory and industrial organization. She is also interested in the effects of economic inequality on cooperation, and the analysis of the development of preferences and skills in preschool children. (website)
Talk: Rational cooperation and reputational effects
Abstract: In repeated prisoners’ dilemmas with a known end, cooperation rates are well above zero. This result cannot be justified from the perspective of non-cooperative game theory based on the assumption that all individuals are payoff maximizers and this is common knowledge. However, a model that mildly relaxes these assumptions by relying on the existence of a small fraction of reciprocators in the population can be sufficient to rationalize cooperation (Kreps et al., 1982). Key is the assumption that individuals are uncertain about the type of their partner in the repeated interaction. The uncertainty introduces a correlation between past actions and beliefs about future actions, which gives rational maximizers an incentive to mimic reciprocators as long as there is scope for future interaction, and gives them a chance to update beliefs about the type of their partner in the course of the interaction. We design a laboratory experiment aimed at uncovering the behavioral relevance of the uncertainty assumption. We find that the presence of reciprocators and the assumption of uncertainty are crucial to explain behavior in our experiment, and provide evidence consistent with rational updating.
Alice is a senior research economist at the Institute of Transport Economics in Norway, where she carries out research in behavioral and experimental economics applied to transport policies. She focuses mostly on individual travel behavior studying how to incentivise the uptake of sustainable transport modes, improve the acceptability of different environmental policies and the role of the sharing economy and new technologies. Alice has a PhD in Economics from the University of Oslo, and was Chazen Visiting scholar at Columbia University, where she applied experimental methods, Game Theory models and econometric models to answer questions at the intersection of Behavioral and Environmental Economics. (website)
Talk: Using behavioral insights to incentivize cycling: Results from a field experiment
Abstract: Motivating active transport is a health and environmental policy priority, and plays an important role in achieving the necessary shift toward a sustainable transport system. Financial incentives to promote cycling are used in many countries, but very few studies document causal effects. Using a randomized controlled trial in the field, we provide causal evidence of the effect of different types of economics incentives on cycling activity in Norway. Participants’ mobility is monitored through an innovative mobile app that registers travel behavior automatically. Results show that both a flat rate and a conditional lottery motivate people to cycle more. Compared to the control group, participants who received an economic incentive cycled 36% more and 18% more often. The conditional lottery appears to be an effective and economically efficient solution and the only treatment with a lasting effect after the incentives were removed.
Anxo is a full professor of Applied Mathematics at Universidad Carlos III de Madrid and founder of the interdisciplinary group on complex systems (GISC). His research deals mostly with the applications of the physics of complex systems to social and biological sciences; he has contributed to the advancement of different fields, from economics to condensed matter physics through ecology and theoretical computer science. (website)
Talk: Games on complex systems: information and agency as mechanisms driving complexity in experiments
Abstract: When games are played on complex systems such as networks, it is easy to guess that the outcome of the game will be complex as well. The question as to what are the mechanisms underlying such complexity is more difficult to address, though. In this talk I will present two specific examples of drivers of complex outcomes in experimental games. I will first present the results of large scale public goods games, played with up to 1000 players simultaneously, showing that the collective behavior depends crucially on the information provided to the participants. Subsequently I will discuss coordination between two types of agents on static vs dynamic networks, illustrating how the possibility of choosing with whom to interact is key to the very diverse collective behaviors arising. Finally, I will try to draw some general lessons about games on complex systems.
Fernando Santos is an Assistant Professor at the University of Amsterdam (Informatics Institute). In his research, he is interested in understanding collective dynamics in complex systems, in explaining the evolution of cooperation and in designing fair/pro-social AI. Fernando received his PhD in Computer Science and Engineering at Instituto Superior Técnico (Lisbon, Portugal) with a thesis on dynamics of reputations and the self-organization of cooperation. Before joining the University of Amsterdam, Fernando was a James S. McDonnell postdoctoral fellow at Princeton University. (website)
Talk: Cooperation and coordination dynamics in collective index insurance
Abstract: Low-income farmers often fall into poverty traps as the specter of extreme weather events discourages them to invest in productive technologies. Insurance offers, in principle, a solution for this conundrum. Group collective index insurance constitutes an alternative to indemnity or individual index insurance and has the potential to alleviate basis risk through within-group informal transfers. The adoption of such products is not straightforward, likely resulting from a combination of utility maximization and peer-influence. In this talk, I will present a model based on evolutionary game theory that aims at capturing individuals’ decision to acquire index insurance in such a complex system. I will show that collective index insurance introduces a coordination dilemma of insurance adoption: socially optimal outcomes are obtained when everyone adopts insurance; however, a minimum fraction of contributors is necessary before the effects of basis risk can be averaged out and the population self-organizes into high levels of insurance adoption. I will further show that additional mechanisms—such as local peer monitoring and defector exclusion—are necessary to stabilize informal transfers and collective index insurance adoption.
Lei Zhou is an Assistant Professor at the Institute of Automation, Chinese Academy of Sciences. Before joining the Institute of Automation, Lei received his PhD at Peking University (Beijing, China) with a thesis about evolutionary game dynamics on complex networks. Lei’s current research interests are evolutionary game dynamics in complex systems, game-theoretic learning, and cooperation.
Talk: Aspiration dynamics generate robust predictions in heterogeneous populations
Abstract: In evolutionary game dynamics, decision-making rules specify what kind of information individuals use and how they process such information to determine future strategies. As the input for decision-making, the information used is likely to affect individuals’ strategy updating, resulting in changes at the population level. Indeed, previous theoretical studies show that evolutionary outcomes are sensitive to model details when individuals imitate the more successful social peers (imitation-based decision-making rules) but are robust when they self-evaluate strategy performance by referring to their own aspirations (self-evaluation-based decision-making rules). However, most studies of self-evaluation-based rules focus on homogeneous population structures where each individual has the same number of neighbors. In this talk, I will introduce our recent work that investigates evolutionary dynamics under self-evaluation-based decision-making rules (i.e., aspiration dynamics) in heterogeneous populations. Our theoretical results show that under weak selection, the condition for one strategy to prevail over the other coincides with the classical condition of risk-dominance. Moreover, this condition holds for all weighted networks and distributions of aspiration levels, and for individualized ways of self-evaluation. Our findings recover previous theoretical results as special cases and demonstrate the universality of the robustness property under aspiration dynamics. Our work thus sheds light on the intrinsic difference between evolutionary dynamics under self-evaluation-based and imitation-based decision-making rules.