We consider an incomplete information network game in which agents are only aware of the identity of their immediate neighbors. They form beliefs about the links of their neighbors (the rest of the network) and play a linear-quadratic effort game to maximize interim payoffs. We establish the existence and uniqueness of Bayesian-Nash equilibria in pure strategies. In equilibrium, agents use local knowledge of their direct connections to make inferences about the complementarity strength of their actions with other agents given by their updated beliefs regarding their walks in the network. Using this and an example we show that under incomplete information, besides network architecture, agent identity plays a crucial role in determining strategic behavior. We also characterize equilibrium behavior under different forms of ex-ante prior beliefs like uniform priors, Erdos-Renyi network generation, and homophilic linkage. Not surprisingly, uniform priors provide similar results similar to degree-based models of incomplete information.
Deciding between apples and oranges has been an age-old question not just for hungry shoppers but within the field of decision-making research. However, very rarely have researchers considered the possibility to reject either and move on to the next shelf. I have previously argued that such a sequential decision making framework is not just essential for understanding foraging animals, but also ecological, real life, behaviour in humans1,2. While it is intuitive that real life decision strategies require temporally extended coherent behaviours2 and rely on prospection, maintained motivation and sequential adaptation, those cognitive and neural processes remain poorly understand. In the first part of my talk I will present our recent cognitive model for sequential search decisions and its underlying neural dynamics3. In the second part I will further expand into another important element of sustained and sequential behaviours, i.e. intrinsic motivation. In particular, I will focus on the circuits fluctuating with motivation to continue pursuing the current task instead of disengaging, showing task general as well as causal evidence. Lastly, I will talk about ongoing work on sequential incremental goal pursuit and how the nature of decision-making changes with goal progress neurally and behaviourally as participants assess whether to give into temptation or frustration.
Abstract:
We provide evidence that classic lottery anomalies like probability weighting and loss aversion are not special phenomena of risk. They also arise (and often with equal strength) when subjects evaluate deterministic, positive monetary payments that have been disaggregated to resemble lotteries. Thus, we find, e.g., apparent probability weighting in settings without probabilities and loss aversion in settings without scope for loss. Across subjects, anomalies in these deterministic tasks strongly predicts the same anomalies in lotteries. These findings suggest that much of the behavior motivating our most important behavioral theories of risk derive from complexity-
driven mistakes rather than true risk preferences.
For more than a decade, economic complexity has been a flagship application of machine learning methods to questions of sustainable growth and development. In this talk I will introduce the main methods used in the field, connect them to their related concepts in AI, and explore recent and upcoming research in economic complexity. I will conclude by discussing new research directions in the field including the use of economic complexity methods to questions of economic history and digital trade.