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Title: Social norms and learning in games
Author: Jindani, Sam
ISNI:       0000 0004 6493 8727
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Duelling The norm of duelling endured for hundreds of years in Europe. In the United Kingdom it disappeared abruptly in the mid-nineteenth century, whereas in France it declined slowly. I present a simple model of social norms that explains these phenomena. The model predicts that the evolution of norms is characterised by tipping, whereby norms can shift suddenly due to shocks, and by a ratchet effect, whereby changes in parameters can cause norms to decline gradually. I show that the model can be supported by an equilibrium of a repeated game, with no special assumptions about preferences. Community enforcement using modal actions I prove two folk theorems for repeated games with random matching. A large group of players is rematched at random each period, so that players who deviate must be sanctioned by third parties. Previous analyses have either relied on strong assumptions about information transmission, or have been limited to equilibria that are not robust to noise or in which players are indifferent. I use a simple construction based on modal actions to obtain results for strict and robust equilibria. Learning repeated-game strategies The literature on boundedly rational learning has tended to focus on stagegame actions. I present a stochastic learning rule for repeated-game strategies. Players form beliefs about their opponent’s strategy based on past actions and best-respond. Occasionally, they make mistakes and experiment, and I show that the equilibrium selected depends on exactly how players make mistakes. Simple specifications of the learning rule yield intuitive selection results: the maxmin, or Rawlsian, outcome; the Nash bargaining solution; the maximum of the sum of payoffs; and a generalisation of risk dominance.
Supervisor: Young, Peyton ; Norman, Thomas Sponsor: Economic and Social Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Game theory ; Economics ; Social norms ; Learning