Discovering Optimal Imitation Strategies

This paper develops a general policy for learning relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or of gestures. The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi- dimensional datasets. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different imitative tasks and controls task reproduction by a full body humanoid robot

Economic Development as Self-Discovery

In the presence of uncertainty about what a country can be good at producing, there can be great social value to discovering costs of domestic activities because such discoveries can be easily imitated. We develop a general-equilibrium framework for a small open economy to clarify the analytical and normative issues. We highlight two failures of the laissez-faire outcome: there is too little investment and entrepreneurship ex ante, and too much production diversification ex post. Optimal policy consists of counteracting these distortions: to encourage investments in the modern sector ex ante, but to rationalize production ex post. We provide some informal evidence on the building blocks of our model.

A MODEL OF DEVELOPMENT OF AGRICULTURAL BIOTECHNOLOGICAL INNOVATIONS: PATENT POLICY ANALYSIS

In this paper, peculiarities of the process of development of agricultural biotechnological innovations are considered, in particular the distinction between R&D races for gene discoveries and subsequent competition for developing their marketable applications in the form of genetically modified (GM) crops, the results of which determine the payoffs of discovering a gene. A formal two-stage model is specified and analyzed with regard to how different patent protection regimes and other government policies affect firm's R&D strategies and the welfare realized from an innovation. We find that different policy measures affect the outcomes of the two stages of biotechnological innovation differently, which leaves some ambiguity as to which patent protection regimes might be strictly preferable. However, general direction of policy improvement is identified.Research and Development/Tech Change/Emerging Technologies,

Learning the optimal buffer-stock consumption rule of Carroll

This article questions the rather pessimistic conclusions of Allen et Carroll (2001) about the ability of consumer to learn the optimal buffer-stock based consumption rule. To this aim, we develop an agent based model where alternative learning schemes can be compared in terms of the consumption behaviour that they yield. We show that neither purely adaptive learning, nor social learning based on imitation can ensure satisfactory consumption behaviours. By contrast, if the agents can form adaptive expectations, based on an evolving individual mental model, their behaviour becomes much more interesting in terms of its regularity, and its ability to improve performance (which is as a clear manifestation of learning). Our results indicate that assumptions on bounded rationality, and on adaptive expectations are perfectly compatible with sound and realistic economic behaviour, which, in some cases, can even converge to the optimal solution. This framework may therefore be used to develop macroeconomic models with adaptive dynamics.Consumption decisions; Learning; Expectations; Adaptive behaviour, Computational economics

Learning the optimal buffer-stock consumption rule of Carroll

This article questions the rather pessimistic conclusions of Allen et Carroll (2001) about the ability of consumer to learn the optimal buffer-stock based consumption rule. To this aim, we develop an agent based model where alternative learning schemes can be compared in terms of the consumption behaviour that they yield. We show that neither purely adaptive learning, nor social learning based on imitation can ensure satisfactory consumption behaviours. By contrast, if the agents can form adaptive expectations, based on an evolving individual mental model, their behaviour becomes much more interesting in terms of its regularity, and its ability to improve performance (which is as a clear manifestation of learning). Our results indicate that assumptions on bounded rationality, and on adaptive expectations are perfectly compatible with sound and realistic economic behaviour, which, in some cases, can even converge to the optimal solution. This framework may therefore be used to develop macroeconomic models with adaptive dynamics.Consumption decisions; Learning; Expectations; Adaptive behaviour; Computational economics

Bayesian Nonparametric Feature and Policy Learning for Decision-Making

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario