Bayes correlated equilibrium and inflationary bias
ii. Dynamic stochastic influences – beyond static model assumptions, real economies are vulnerable to unpredictable shocks that require continuous policy adaption. Thus, a more adaptive mechanism to policy approach is called upon in order to manage inflation effectively.
It is clear from this survey of economic literature on the problem of inflationary bias that no attractive and empirically reliable solution has been found as to how to reach a stable equilibrium.
Alternative methodology
In the previous section, the inefficient outcome was explicitly displayed via the noncooperative game played between the monetary authorities and the public. Non-cooperative games often lead to inefficient outcomes due to the lack of enforceable agreements and dominant strategy of defection, resulting in inefficient monetary policy. 6 Geanakoplos 1994 proposes that, within a non-cooperative environment, cooperation between players can still emerge. He demonstrates that, when bounded rationality 7 is factored into decision-making, a more complex dynamic emerges, 8 countering the paradox where hyper-rational behaviour often leads to Pareto suboptimal outcomes. 9 By allowing for a small chance that players might not act in their immediate self interest in the final round, strategic uncertainty is introduced. This uncertainty acts as a catalyst for cooperative behaviour as players employ backwards induction, 10 adjusting their strategies in the earlier stages to align with the potential for end-game cooperation. This strategic adjustment is not confined to the final rounds but cascades through the entire timeline of the game, fundamentally altering the natural interaction from one of inevitable defection to one where cooperation becomes strategy. Considering the invaluable insights from Geanakoplos 1994 regarding the emergence of cooperative behaviour in non-cooperative settings, we build on the topic of strategy optimization in non- cooperative settings and propose the application of Bayes Correlated Equilibrium (BCE), created by Bergemann & Morris 2013. It is an extension of Aumann 1987 via the implementation of Bayesian updating. This extension allows players to adapt their strategies by processing (and reacting to) their own private signals and posterior beliefs, based on the common probability assessments of these signals within a game-theoretic network. This refinement enhances the predictive precision of equilibrium outcomes under conditions of asymmetric information. BCE posits that strategies can become ‘correlated’ through observable public signals, thereby ‘nudging’ economic agents (EAs) towards a mutually beneficial equilibrium. 11 6 Without a mechanism for commitment, both parties may default to strategies that prioritise short term gains, leading to a suboptimal equilibrium. 7 Geanakoplos 1994 achieves this by introducing a slight probability of non-optimal behaviour, i.e. a relaxation of pure rationality in the game’s final round. 8 Where agents’ decisions are not solely based on their preferences, but also the limitations of their cognitive processes and the information available to them. 9 Kreps & Wilson 1982 argue that incomplete information can lead rational players to adopt strategies with the classic Nash equilibrium, countering the assumption of full rationality to achieve better outcomes. 10 Selten 1975 emphasizes the importance of considering the sequential nature of decisions in games, where the anticipation of future strategy adjustments can significantly influence earlier decisions, leading to a cooperative strategy formation from the outset. 11 Hart & Mas-Colell 2003 describe how adaptive responses to observable actions can evolve individual strategies towards a Nash equilibrium, emphasizing the critical role of public signals in shaping decisions.
209
Made with FlippingBook flipbook maker