Narrow Results

Much experimental effort has been expended in attempts to establish the relative superiority of Expected Utility theory and the many recently-developed alternatives as descriptions of the behaviour of subjects in risky choice decision problems. The cumulative evidence shows clearly that there is a great deal of noise in the experimental data, which makes it difficult to identify the ‘best’ description of such behaviour. This paper reports on an experiment which seeks to determine whether such noise is relatively transitory and decays with experience and repetition, and thus whether a clearly ‘best’ theory emerges as a result of such repetition. We find that for some subjects this does indeed appear to be the case, while for other subjects the noise remains high and the identification of the underlying preference function remains difficult.

This paper investigates whether some part of the preference reversal phenomenon can be attributed to errors in the responses of subjects in experiments. Such errors have been well documented in other investigations of behaviour in risky decision problems, but their relevance to the preference reversal phenomenon has not been explored. Building on earlier work, we develop an extended error model and apply it to the results of an experiment in which subjects tackle risky choice problems on five separate occasions. In this experiment subjects had to answer choice questions in three occasions and to state selling and buying prices in the remaining two occasions. Our results indicate that scale compatibility can be ruled out as a significant sole explanation of the preference reversal phenomenon. Moreover, we can show that a considerable fraction of observed preference reversals can be classified as pricing errors, whereas choice errors turn out to play a minor role.

Two recent papers, Harless and Camerer(1994) and Hey and Orme(1994) are both addressed to the same question: which is the ‘best’ theory of decision making under risk? As an essential part of their separate approaches to an answer to this question, both sets of authors had to make an assumption about the underlying stochastic nature of their data. In this context this implied an assumption about the ‘errors’ made by the subjects in the experiments generating the data under analysis. The two different sets of authors adopted different assumptions: the purpose of this current paper is to compare and contrast these two different error stories — in an attempt to discover which of the two is ‘best’.

Many, if not all, of the recent theories of decision making under risk, that have been developed in the light of experimentally observed violations of Expected Utility theory, are essentially deterministic in nature, yet it is clear that actual decision making contains a random or error component. This paper surveys the assumptions that have been employed in previous analyses of such experimental data, and tries to find better explanations (both from an economic and an econometrics point of view), of such errors. Hopefully, such work will lead to a unified theory, in which the stochastic component is an integral part.