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on Utility Models and Prospect Theory |
| By: | Michèle Cohen (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Jean-Marc Tallon (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris); Jean-Christophe Vergnaud (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, CERSES - Centre de recherche sens, ethique, société - CNRS : UMR8137 - Université Paris Descartes) |
| Abstract: | We report in this paper the result of three experiments on risk, ambiguity and time attitude. The first two differed by the population considered (students vs. general population) while the third one used a different protocol and concerned students and portfolio managers. We find quite a lot of heterogeneity at the individual level. Of principal interest was the elicitation of risk, time and ambiguity attitudes and the relationship among these (model free) measures. We find that on the student population, there is essentially no correlation. A non negligible fraction of the population behaves in an extremely cautious manner in the risk and ambiguity domain. When we drop this population from the sample, the correlation between our measures is also non significant. We also raise three questions linked to measurement of ambiguity attitudes that come out from our data sets. |
| Keywords: | Experiments, Risk aversion, Impatience, Imprecision Aversion. |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00502820_v1&r=upt |
| By: | Mario Fedrizzi (DISA, Faculty of Economics, Trento University); Michele Fedrizzi; Ricardo Alberto Marques Pereira (DISA, Faculty of Economics, Trento University); Matteo Brunelli |
| Abstract: | In this paper we present an overview of the soft consensus model in group decision making and we investigate the dynamical patterns generated by the fundamental pairwise preference interactions on which the model is based. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual preferences. The structure of fuzzy relations is present at both the individual and the collective levels of description of the soft consensus model: pairwise preference intensities between alternatives at the individual level, and pairwise interaction coefficients between decision makers at the collective level. The collective measure of dissensus is based on non linear scaling functions of the linguistic quantifier type and expresses the degree to which most of the decision makers disagree with respect to their preferences regarding the most relevant alternatives. The graded notion of consensus underlying the dissensus measure is central to the dynamical unfolding of the model. The original formulation of the soft consensus model in terms of standard numerical preferences has been recently extended in order to allow decision makers to express their preferences by means of triangular fuzzy numbers. An appropriate notion of distance between triangular fuzzy numbers has been chosen for the construction of the collective dissensus measure. In the extended formulation of the soft consensus model the extra degrees of freedom associated with the triangular fuzzy preferences, combined with non linear nature of the pairwise preference interactions, generate various interesting and suggestive dynamical patterns. In the present paper we investigate these dynamical patterns which are illustrated by means of a number of computer simulations. |
| Date: | 2010–07 |
| URL: | http://d.repec.org/n?u=RePEc:trt:disawp:1004&r=upt |
| By: | Dahlan, Rolan Mauludy; Situngkir, Hokky |
| Abstract: | The integration process between evolutionary approach and conventional economic analysis is very essential for the next development of economic studies, especially in the fundamental concepts of modern economics: supply and demand analysis. In this presentation, we use the concept of meme to explore evolution of demand. This study offers an evolutionary model of demand, which views utility as a function of the distance between the two types of sequences of memes (memeplex), which represent economic product and consumer preference. It is very different from the conventional approach of demand, which only views utility as a function of quantity. This modification provides an opportunity to see innovation and transformation of consumer preferences in the demand perspective. Innovation is seen as a change in sequence of memes in economic products, while the transformation of consumer behavior is defined as a change in the aligning memes of consumer preference. Demand quantity is the result of the selection process. This model produces some interesting characteristics, such as: (i) quantitative and qualitative properties of evolution of demand, (ii) relationship between consumer behavior and properties of evolution of demand that occurred and (iii) power law on the distribution of product lifetime. At the end we show the improvement of utility function, in the concept of meme, might create a new landscape for the further development of economics. |
| Keywords: | Evolutionary economics; memetics; demand; evolution; innovation; transformation of consumer behavior |
| JEL: | D11 D91 D40 O33 L16 E11 L15 O31 E20 |
| Date: | 2010–07–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:24159&r=upt |
| By: | Teng , Jimmy |
| Abstract: | Bayesian rational prior equilibrium requires agent to make rational statistical predictions and decisions, starting with first order non informative prior and keeps updating with statistical decision theoretic and game theoretic reasoning until a convergence of conjectures is achieved. The main difference between the Bayesian theory of games and the current games theory are: I. It analyzes a larger set of games, including noisy games, games with unstable equilibrium and games with double or multiple sided incomplete information games which are not analyzed or hardly analyzed under the current games theory. II. For the set of games analyzed by the current games theory, it generates far fewer equilibria and normally generates only a unique equilibrium and therefore functions as an equilibrium selection and deletion criterion and, selects the most common sensible and statistically sound equilibrium among equilibria and eliminates insensible and statistically unsound equilibria. III. It differentiates between simultaneous move and imperfect information. The Bayesian theory of games treats sequential move with imperfect information as a special case of sequential move with observational noise term. When the variance of the noise term approaches its maximum such that the observation contains no informational value, there is imperfect information (with sequential move). IV. It treats games with complete and perfect information as special cases of games with incomplete information and noisy observation whereby the variance of the prior distribution function on type and the variance of the observation noise term tend to zero. Consequently, there is the issue of indeterminacy in statistical inference and decision making in these games as the equilibrium solution depends on which variances tends to zero first. It therefore identifies equilibriums in these games that have so far eluded the classical theory of games. |
| Keywords: | Games Theory; Bayesian Statistical Decision Theory; Prior Distribution Function; Conjectures; Subjective Probabilities |
| JEL: | C02 C11 C72 |
| Date: | 2010–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:24189&r=upt |