25,411 research outputs found

    Policy with Dispersed Information

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    Information regarding economic fundamentals is widely dispersed in society, is only imperfectly aggregated through prices or other indicators of aggregate activity, and can not be centralized by the government or any other institution. In this paper we seek to identify policies that can improve the decentralized use of such dispersed information without requiring the government to observe this information. We show that this can be achieved by appropriately designing the contingency of taxation on ex-post public information regarding the realized fundamentals and aggregate activity. When information is common (as in the Ramsey literature) or when agents have private information only about idiosyncratic shocks (as in the Mirrlees literature), the contingency on fundamentals alone suffices for efficiency. When instead agents have private information about aggregate shocks, the contingency on aggregate activity is crucial. An appropriate combination of the two contingencies permits the government to: (i) dampen the impact of noise and hence reduce non-fundamental volatility, without also dampening the impact of fundamentals; (ii) induce agents to internalize informational externalities, and hence improve the speed of social learning; (iii) restore a certain form of constrained efficiency in the decentralized use of information; (iv) guarantee that welfare increases with the provision of any additional information.Optimal policy, private information, complementarities, information externalities, social learning, efficiency

    Policy with Dispersed Information

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    This paper studies policy in a class of economies in which information about commonly-relevant fundamentals -- such as aggregate productivity and demand conditions -- is dispersed and can not be centralized by the government. In these economies, the decentralized use of information can fail to be efficient either because of discrepancies between private and social payoffs, or because of informational externalities. In the first case, inefficiency manifests itself in excessive non-fundamental volatility (overreaction to common noise) or excessive cross-sectional dispersion (overreaction to idiosyncratic noise). In the second case, inefficiency manifests itself in suboptimal social learning (low quality of information contained in macroeconomic data, financial prices, and other indicators of economic activity). In either case, a novel role for policy is identified: the government can improve welfare by manipulating the incentives agents face when deciding how to use their available sources of information. Our key result is that this can be achieved by appropriately designing the contingency of marginal taxes on aggregate activity. This contingency permits the government to control the reaction of equilibrium to different types of noise, to improve the quality of information in prices and macro data, and, in overall, to restore efficiency in the decentralized use of information.

    A nonlinear structural model for volatility clustering

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    A simple nonlinear structural model of endogenous belief heterogeneity is proposed. News about fundamentals is an IID random process, but nevertheless volatility clustering occurs as an endogenous phenomenon caused by the interaction between different types of traders, fundamentalists and technical analysts. The belief types are driven by adaptive, evolutionary dynamics according to the success of the prediction strategies as measured by accumulated realized profits, conditioned upon price deviations from the rational expectations fundamental price. Asset prices switch irregularly between two different regimes --periods of small price fluctuations and periods of large price changes triggered by random news and reinforced by technical trading -- thus, creating time varying volatility similar to that observed in real financial data.

    Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

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    The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

    Complexity, Evolution and Learning: a simple story of heterogeneous expectations and some empirical and experimental validation.

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    This note discusses complexity models in economics. A key feature of these models is that agents have heterogeneous expectations, disciplined by adaptive learning and evolutionary selection. Agents adapt their rules based upon past observations and switch between different forecasting heuristics based upon strategy performance. We discuss how these models match empirical facts as well as laboratory experiments with human subjects and how this approach may tame the ``wilderness of bounded rationality''.
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