Intelligent systems play a crucial role in our everyday life. Yet there is still no general concept for designing such systems. Systems are designed on a case-by-case basis, mostly in an ad-hoc fashion lacking any kind of general design strategy. My research aims to solve the above problem by unifying game, agent, and evolution theory using a special concept of rationality: bounded optimality [...

Selection methods in Evolutionary Algorithms, including Genetic Algorithms, Evolution Strategies (ES) and Evolutionary Programming, (EP) are compared by observing the rate of convergence on three idealised problems. The rst considers selection only, the second introduces mutation as a source of variation, the third also adds in evaluation noise. Fitness proportionate selection su ers from scali...

Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper we propose general-to-specific model selection procedures to overcome these limitations. After showing that single-equation ...

Monte-Carlo Tree Search (MCTS) has proved a remarkably effective decision mechanism in many different game domains, including computer Go and general game playing (GGP). However, in GGP, where many disparate games are played, certain type of games have proved to be particularly problematic for MCTS. One of the problems are game trees with so-called optimistic moves, that is, bad moves that supe...

A new minimax model on optimal portfolio selection with uncertainty of both randomness and estimation in inputs is established and the corresponding optimal portfolio is derived analytically. Based on this result, a sufficient condition for the existence and uniqueness of a nonnegative equilibrium price system under which the total demand and supply of each asset are equal is provided and an ex...

While current artificial systems must be custom tailored to operate within a single domain of expertise, the human mind readily adapts to multiple domains. In moving toward artificial systems that display similar flexibility, we become interested in how efforts to learn about the mind can support the development of flexible artificial systems, and how the development of these systems can contri...

Standard models of population dynamics focus on the interaction, survival, and extinction of the competing species individually. Real ecological systems, however, are characterized by an abundance of species (or strategies, in the terminology of evolutionary-game theory) that form intricate, complex interaction networks. The description of the ensuing dynamics may be aided by studying associati...

This study was designed to assess sex-related differences in the selection of an appropriate strategy when facing novelty. A simple visuo-spatial task was used to investigate exploratory behavior as a specific response to novelty. The exploration task was followed by a visual discrimination task, and the responses were analyzed using signal detection

Many decisions have to be made on the basis of knowledge about correlational structures in the environment. It has been found that people with a low working memory capacity perform better in a covariation detection task (Kareev, Lieberman, & Lev, 1997). This has been attributed to the assumption that they can only consider smaller samples which are more likely to bear a correlation parameter th...

In this paper we propose a bottom-up model of decision strategy selection, We assume that working memory capacity plays a crucial role in shaping predecisional information processing. Moreover, we assume that the often postulated repertoire of choice strategies can be explained as an expression of one strategy performed with different working memory limits. A mathematical model of this process ...