It is becoming increasingly common to accept that heterogeneity of preferences is an appropriate approach to describe aggregate experimental data on risky choice. We propose a parametric form of utility consistent with Markowitz's (1952) hypotheses as a useful model to consider. This value function exhibits the fourfold attitude to risk and can also capture different combinations of risk attitudes and higher-order preferences. Moreover, it can be combined with probability weighting functions as well as with other value functions as part of mixture models that capture heterogeneity of preferences. We employ data from three recent experimental studies and show that this model can contribute to the explanation of their findings.
In this paper, we analyse higher-order risky choices by the representative cumulative prospect theory (CPT) decision maker from three alternative reference points. These are the status quo, average payout and maxmin. The choice tasks we consider in our analysis include binary risks, and are the ones employed in the experimental literature on higher order risk preferences. We demonstrate that the choices made by the representative subject depend on the reference point. If the reference point is the status quo and the lottery choices exhibit symmetric risk, we demonstrate that there is no third order reflection effect of lottery choices but there is a fourth order reflection effect. When the average payout is the reference point, we demonstrate that any third or fourth order lottery choice is possible dependent upon the lottery payoffs. However, under the assumption of maxmin reference point, the risky choices are prudent and temperate. In addition to these results, our analysis reveals that the representative CPT subject can choose combinations of second with third and fourth order risky options that differ from those in other major models of decision under risk. We contrast our theoretical predictions with the empirical results reported in the literature on higher order risk preferences and are able to reconcile some conflicting experimental evidence.
This paper focuses on comparing individual and group decision making, in a stochastic inter-temporal problem in two decision environments, namely risk and ambiguity. Using a consumption/saving laboratory experiment, we investigate behaviour in four treatments: (1) individual choice under risk; (2) group choice under risk; (3) individual choice under ambiguity and (4) group choice under ambiguity. Comparing decisions within and between decision environments, we find an anti-symmetric pattern. While individuals are choosing on average closer to the theoretical optimal predictions, compared to groups in the risk treatments, groups tend to deviate less under ambiguity. Within decision environments, individuals deviate more when they choose under ambiguity, while groups are better planners under ambiguity rather than under risk. We argue that the results might be driven by differences in the levels of ambiguity and risk attitudes between individuals and groups, extending the frequently observed pattern of groups behaving closer to risk and ambiguity neutrality, to its dynamic dimension.
We report on an experimental investigation of the emergence of Spontaneous Order, the idea that societies can coordinate , without government intervention, on a form of society that is good for its citizens, as described by Adam Smith. Our experimental design is based on a production game with a convex input provision possibility frontier, where subjects have to choose a point on this frontier. We start with a simple society consisting of just two people, two inputs, one final good and in which the production process exhibits returns to specialisation. We then study more complex societies by increasing the size of the society (groups of 6 and 9 subjects) and the number of inputs (6 and 9 inputs respectively), as well as the combinations of inputs that each subject can provide. This form of production can be characterised as a cooperative game, where the Nash equilibrium predicts that the optimal outcome is achieved when each member of this society specialises in the provision of a single input. Based on this framework, we investigate whether Spontaneous Order can emerge, without it being imposed by the government. We find strong evidence in favour of the emergence of Spontaneous Order, with communication being an important factor. Using text classification algorithms (Multinomial Naive Bayes) we quantitatively analyse the available chat data and we provide insight into the kind of communication that fosters specialisation in the absence of external involvement. We note that, while communication has been shown to foster coordination in other contexts (for example, in public goods games, market entry games and competitive coordination games) this contribution is in the context of a production game where specialisation is crucial.
This chapter discusses the way that three distinct fields, decision theory, game theory and computer science, can be successfully combined in order to optimally design economic experiments. Using an example of cooperative game theory (the Stag-Hunt game), the chapter presents how the introduction of ambiguous beliefs and attitudes towards ambiguity in the analysis can affect the predicted equilibrium. Based on agent-based simulation methods, the author is able to tackle similar theoretical problems and thus to design experiments in such a way that they will produce useful, unbiased and reliable data.
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