A computational theory of willingness to exchange, ESRI working paper no. 477, January 2014

A new model of exchange is presented following Marr’s conception of a “computational theory”. The model combines assumptions from perceptual theory and economic theory to develop a highly generalised formal model. The approach departs from previous models by focussing not on how ownership alters preferences, but instead on difficulties inherent in the process of exchange in real markets. Agents treat their own perceptual uncertainty when valuing a potential exchange item as a signal regarding the variability of potential bids and offers. The analysis shows how optimising agents, with no aversion to risk or loss, will produce an endowment effect of variable degree, in line with empirical findings. The model implies that the endowment effect is not a laboratory finding that may not occur in real markets, but rather a market phenomenon that may not occur in the laboratory

Stockholding: Recent Lessons from Theory and Computations

Household-level portfolio data show a tendency of the majority of households in each country to hold no stocks despite a historical expected-return premium on equity relative to riskless assets. This paper first explains why such a tendency constitutes a puzzle in economic theory (the "stockholding puzzle"). It discusses why simple popular notions regarding the source of non-participation (risk aversion, risky labor income, and borrowing constraints) are not confirmed by careful analysis of portfolio models and presents recent conclusions on what causes non-participation. Based on this, it revisits the popular view on non-participation and shows how it can be qualified to be consistent with lessons from economic theory. It also explains how this view can be extended to account for exits from the stock market and for limited diversification. Then, the paper describes three unsolved empirical puzzles concerning the share of stocks in portfolios of households that do participate in the stock market. It points to basic underlying mechanisms producing these theoretical results and discusses briefly possible future directions for research that may help resolve the puzzles. Finally, the paper draws lessons for practitioners interested in expanding the stockholder base.

Decision-making and strategic thinking through analogies

When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? Consider the chess game: how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies and abstract roles are crucial to solving these landmark problems. We present a proof-of-concept model, in the form of a computational architecture, which may be able to account for many crucial aspects of human intuition, such as (i) concentration of attention to relevant aspects, (ii) \ud how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, and (iv) a state of meaningful anticipation over how a global scenario \ud may evolve