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Abstract

In most macroeconomic models, time is infinite. Agents are endowed with rational expectations including the cognitive ability to solve complex infinite-horizon planning problems. This is a heroic assumption; but when does it matter? In “Monetary Policy Analysis When Planning Horizons Are Finite,” Michael Woodford reconsiders this unrealistic feature, introduces a novel bounded-rationality framework to address it, and explores under what circumstances this affects the policy conclusions of the standard New Keynesian paradigm. Woodford develops a new cognitive framework in which agents transform their infinite-horizon problem into a sequence of simpler, finite-horizon ones. The solution method used by the agent is to backward induct over a finite set of periods given some perceived value function he has assigned to his perceived terminal nodes. This solution method seems quite natural; in fact, Woodford is motivated by a beautiful analogy to how state-of-the-art artificial intelligence (AI) programs play the games of chess or go. Take chess—a gamewith a finite strategy space and thereby in theory solvable via backward induction. In practice, however, the space of strategies is so large that solving the game in this fashion would require unfathomableprocessing power. Consider then themost effectiveAI programs. A typical decision-making process may be described as follows: at each turn, the machine looks forward at all possible moves for both itself and its opponent a finite number of turns, thereby creating a decision tree with finite nodes. It assigns a value to each of the different possible terminal nodes; these values may be based on past experience or data. Finally, given these terminal node values, the machine backward