A group decision-making methodology with incomplete individual beliefs applied to e-Democracy

We consider the situation where there are several alternatives for investing a quantity of money to achieve a set of objectives. The choice of which alternative to apply depends on how citizens and political representatives perceive that such objectives should be achieved. All citizens with the right to vote can express their preferences in the decision-making process. These preferences may be incomplete. Political representatives represent the citizens who have not taken part in the decision-making process. The weight corresponding to political representatives depends on the number of citizens that have intervened in the decision-making process. The methodology we propose needs the participants to specify for each alternative how they rate the different attributes and the relative importance of attributes. On the basis of this information an expected utility interval is output for each alternative. To do this, an evidential reasoning approach is applied. This approach improves the insightfulness and rationality of the decision-making process using a belief decision matrix for problem modeling and the Dempster?Shafer theory of evidence for attribute aggregation. Finally, we propose using the distances of each expected utility interval from the maximum and the minimum utilities to rank the alternative set. The basic idea is that an alternative is ranked first if its distance to the maximum utility is the smallest, and its distance to the minimum utility is the greatest. If only one of these conditions is satisfied, a distance ratio is then used.

Strategy-proof choice of acts : a preliminary study

We model social choices as acts mapping states of the world to (social) outcomes. A (social choice) rule assigns an act to every profile of subjective expected utility preferences over acts. A rule is strategy-proof if no agent ever has an incentive to misrepresent her beliefs about the world or her valuation of the outcomes; it is ex-post efficient if the act selected at any given preference profile picks a Pareto-efficient outcome in every state of the world. We show that every two-agent ex-post efficient and strategy-proof rule is a top selection: the chosen act picks the most preferred outcome of some (possibly different) agent in every state of the world. The states in which an agent’s top outcome is selected cannot vary with the reported valuations of the outcomes but may change with the reported beliefs. We give a complete characterization of the ex-post efficient and strategy-proof rules in the two-agent, two-state case, and we identify a rich class of such rules in the two-agent case.

Strategic Voting in Proportional Systems: The Case of Finland. IHS Political Science Series No. 138, September 2014

Voters try to avoid wasting their votes even in PR systems. In this paper we make a case that this type of strategic voting can be observed and predicted even in PR systems. Contrary to the literature we do not see weak institutional incentive structures as indicative of a hopeless endeavor for studying strategic voting. The crucial question for strategic voting is how institutional incentives constrain an individual’s decision-making process. Based on expected utility maximization we put forward a micro-logic of an individual’s expectation formation process driven by institutional and dispositional incentives. All well-known institutional incentives to vote strategically that get channelled through the district magnitude are moderated by dispositional factors in order to become relevant for voting decisions. Employing data from Finland – because of its electoral system a particularly hard testing ground - we find considerable evidence for observable implications of our theory.

Strategy-proof choice of acts : a preliminary study

We model social choices as acts mapping states of the world to (social) outcomes. A (social choice) rule assigns an act to every profile of subjective expected utility preferences over acts. A rule is strategy-proof if no agent ever has an incentive to misrepresent her beliefs about the world or her valuation of the outcomes; it is ex-post efficient if the act selected at any given preference profile picks a Pareto-efficient outcome in every state of the world. We show that every two-agent ex-post efficient and strategy-proof rule is a top selection: the chosen act picks the most preferred outcome of some (possibly different) agent in every state of the world. The states in which an agent’s top outcome is selected cannot vary with the reported valuations of the outcomes but may change with the reported beliefs. We give a complete characterization of the ex-post efficient and strategy-proof rules in the two-agent, two-state case, and we identify a rich class of such rules in the two-agent case.

Equity weights in the allocation of health care: the rank-dependent QALY model

This paper introduces the rank-dependent quality-adjusted life-years (QALY) model, a new method to aggregate QALYs in economic evaluations of health care. The rank-dependent QALY model permits the formalization of influential concepts of equity in the allocation of health care, such as the fair innings approach, and it includes as special cases many of the social welfare functions that have been proposed in the literature. An important advantage of the rank-dependent QALY model is that it offers a straightforward procedure to estimate equity weights for QALYs. We characterize the rank-dependent QALY model and argue that its central condition has normative appeal. (C) 2003 Elsevier B.V. All rights reserved.

Increasing uncertainty: A definition

We present a definition of increasing uncertainty, in which an elementary increase in the uncertainty of any act corresponds to the addition of an 'elementary bet' that increases consumption by a fixed amount in (relatively) 'good' states and decreases consumption by a fixed (and possibly different) amount in (relatively) 'bad' states. This definition naturally gives rise to a dual definition of comparative aversion to uncertainty. We characterize this definition for a popular class of generalized models of choice under uncertainty.

Supermodularity and risk aversion

In this paper, we consider the relationship between supermodularity and risk aversion. We show that supermodularity of the certainty equivalent implies that the certainty equivalent of any random variable is less than its mean. We also derive conditions under which supermodularity of the certainty equivalent is equivalent to aversion to mean-preserving spreads in the sense of Rothschild and Stiglitz. (c) 2006 Elsevier B.V. All rights reserved.

Sensor Planning for Bayesian Nonparametric Target Modeling

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns.

Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time.

Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean currents obtained by moored buoys. A sweep line algorithm is developed for applications where the sensor control space is continuous and unconstrained. Synthetic simulations as well as physical experiments with ground robots and a surveillance camera are conducted to evaluate the performance of the sweep line algorithm. Moreover, a lexicographic algorithm is designed based on the cumulative lower bound of the novel information theoretic functions, for the scenario where the sensor dynamics are constrained. Numerical experiments with real data collected from indoor pedestrians by a commercial pan-tilt camera are performed to examine the lexicographic algorithm. Results from both the numerical simulations and the physical experiments show that the three sensor planning algorithms proposed in this dissertation based on the novel information theoretic functions are superior at learning the target kinematics with

little or no prior knowledge

Risk Preferences and the Role of Emotions

There is a large volume of research showing that emotions have relevant effects on decision-making. We contribute to this literature by experimentally investigating the impact of four specific emotional states - joviality, sadness, fear, and anger - on risk attitudes. In order to do so, we fit two models of behavior under risk: the Expected Utility model (EU) and the Rank Dependent Expected Utility model (RDEU), assuming several functional forms of the weighting function. Our results indicate that all emotional states mitigate risk aversion. Furthermore, we show that there are some differences across gender and participants' experience in laboratory experiments.

Pure higher-order effects in the portfolio choice model

This paper examines the effects of higher-order risk attitudes and statistical moments on the optimal allocation of risky assets within the standard portfolio choice model. We derive the expressions for the optimal proportion of wealth invested in the risky asset to show they are functions of portfolio returns third- and fourth-order moments as well as on the investor’s risk preferences of prudence and temperance. We illustrate the relative importance that the introduction of those higher-order effects have in the decision of expected utility maximizers using data for the US.

Second-Order Risk Constraints

This paper discusses how numerically imprecise information can be modelled and how a risk evaluation process can be elaborated by integrating procedures for numerically imprecise probabilities and utilities. More recently, representations and methods for stating and analysing probabilities and values (utilities) with belief distributions over them (second order representations) have been suggested. In this paper, we are discussing some shortcomings in the use of the principle of maximising the expected utility and of utility theory in general, and offer remedies by the introduction of supplementary decision rules based on a concept of risk constraints taking advantage of second-order distributions.

Démocratie et justice sociale : Arrow-Harsanyi- Rawls-Sen, un quatuor dissonant

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Absolute Inequality and Violent Property Crime

Rational choice models argue that income inequality leads to a higher expected utility of crime and thus generates incentives to engage in illegal activities. Yet, the results of empirical studies do not provide strong support for this theory; in fact, Neumayer provides apparently strong evidence that income inequality is not a significant determinant of violent property crime rates when a representative sample is used and country specific fixed effects are controlled for. An important limitation of this and other empirical studies on the subject is that they only consider proportional income differences, even though in rational choice models absolute difference in legal and illegal incomes determine the expected utility of crime. Using the same methodology and data as Neumayer, but using absolute inequality measures rather than proportional ones, this paper finds that absolute income inequality is a statistically significant determinant of robbery and violent theft rates. This result is robust to changes in sample size and to different absolute inequality measures, which not only implies that inequality is an important correlate of violent property crime rates but also suggests that absolute measures are preferable when the impact of inequality on property crime is studied.

Démocratie et justice sociale : Arrow-Harsanyi- Rawls-Sen, un quatuor dissonant

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Attitudes towards risk in financial decision making

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