scholarly journals Voter Preferences, Polarization, and Electoral Policies

2014 ◽  
Vol 6 (4) ◽  
pp. 203-236 ◽  
Author(s):  
Yuichiro Kamada ◽  
Fuhito Kojima
Keyword(s):  
Utility Function ◽  
Utility Functions ◽  
Economic Policies ◽  
Probabilistic Voting ◽  
Voter Preferences ◽  
Religious Issues ◽  
Voting Model

In most variants of the Hotelling-Downs model of election, it is assumed that voters have concave utility functions. This assumption is arguably justified in issues such as economic policies, but convex utilities are perhaps more appropriate in others, such as moral or religious issues. In this paper, we analyze the implications of convex utility functions in a two-candidate probabilistic voting model with a polarized voter distribution. We show that the equilibrium policies diverge if and only if voters' utility function is sufficiently convex. If two or more issues are involved, policies converge in “concave issues” and diverge in “convex issues.” (JEL D72)

scholarly journals Strategic Ambiguity with Probabilistic Voting

2019 ◽  
Vol 31 (4) ◽  
pp. 626-641
Author(s):  
Yasushi Asako
Keyword(s):  
Political Parties ◽  
Deterministic Model ◽  
Jel Classification ◽  
Utility Functions ◽  
Probabilistic Voting ◽  
Strategic Ambiguity ◽  
Voting Model

Political parties and candidates usually prefer making ambiguous promises. This study identifies the conditions under which candidates choose ambiguous promises in equilibrium, given convex utility functions of voters. The results show that in a deterministic model, no equilibrium exists when voters have convex utility functions. However, in a probabilistic voting model, candidates make ambiguous promises in equilibrium when (i) voters have convex utility functions, and (ii) the distribution of voters’ most preferred policies is polarized. JEL Classification: D71, D72

DECISION ANALYSIS WITH MULTIPLE OBJECTIVES IN A FRAMEWORK FOR EVALUATING IMPRECISION

2005 ◽  
Vol 13 (05) ◽  
pp. 495-509 ◽  
Author(s):  
ARON LARSSON ◽  
JIM JOHANSSON ◽  
LOVE EKENBERG ◽  
MATS DANIELSON
Keyword(s):  
Decision Tree ◽  
Utility Function ◽  
Evaluation Method ◽  
Convex Sets ◽  
Utility Functions ◽  
Probability Measures ◽  
Decisions Under Risk ◽  
Closed Intervals ◽  
Pros And Cons ◽  
Tree Evaluation

We present a decision tree evaluation method for analyzing multi-attribute decisions under risk, where information is numerically imprecise. The approach extends the use of additive and multiplicative utility functions for supporting evaluation of imprecise statements, relaxing requirements for precise estimates of decision parameters. Information is modeled in convex sets of utility and probability measures restricted by closed intervals. Evaluation is done relative to a set of rules, generalizing the concept of admissibility, computationally handled through optimization of aggregated utility functions. Pros and cons of two approaches, and tradeoffs in selecting a utility function, are discussed.

scholarly journals Comparing utility functions between risky and riskless choice in rhesus monkeys

2021 ◽  
Author(s):  
Philipe M. Bujold ◽  
Simone Ferrari-Toniolo ◽  
Leo Chi U Seak ◽  
Wolfram Schultz
Keyword(s):  
Utility Function ◽  
Utility Theory ◽  
Utility Maximization ◽  
Choice Behavior ◽  
Expected Utility Theory ◽  
Utility Functions ◽  
Random Utility Maximization ◽  
Risky Choices ◽  
Neuronal Mechanisms ◽  
Subjective Value

AbstractDecisions can be risky or riskless, depending on the outcomes of the choice. Expected Utility Theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the option’s value and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of macaque monkeys in riskless and risky decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for riskless and risky choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

Power Law with Exponential Cut‐off Utility

Metamorphosis ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 26-32
Author(s):  
Afreen Arif H. ◽  
T.P.M. Pakkala
Keyword(s):  
Risk Aversion ◽  
Utility Function ◽  
Power Law ◽  
Risk Preferences ◽  
Portfolio Allocation ◽  
Utility Functions ◽  
Relative Risk Aversion ◽  
Optimal Solutions ◽  
New Class ◽  
Exponential Power

Most of the utility functions studied earlier concentrated on properties of risk aversion. In this article, the authors have introduced a new class of utility function called the Power Law with Exponential Cut-off (PLEC) utility function, which exhibits all the absolute and relative risk aversion and risk loving preferences of individuals, under various conditions. It generalises and encompasses other systems of utility functions like that of exponential power. Certain properties of this utility function are discussed. Sensitivity analysis exhibits different portfolio allocations for various risk preferences. The analysis also shows that arbitrary risk preferences may lead to biased risk response estimates. Performance of PLEC utility function in portfolio allocation problem is demonstrated through numerical examples. This is evaluated through optimal solutions.

Autonomous Vehicle-Target Assignment: A Game-Theoretical Formulation

2007 ◽  
Vol 129 (5) ◽  
pp. 584-596 ◽  
Author(s):  
Gürdal Arslan ◽  
Jason R. Marden ◽  
Jeff S. Shamma
Keyword(s):  
Utility Function ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Utility Functions ◽  
Fading Memory ◽  
Theoretical Formulation ◽  
Design Procedures ◽  
Target Assignment ◽  
Negotiation Mechanism ◽  
Adaptive Play

We consider an autonomous vehicle-target assignment problem where a group of vehicles are expected to optimally assign themselves to a set of targets. We introduce a game-theoretical formulation of the problem in which the vehicles are viewed as self-interested decision makers. Thus, we seek the optimization of a global utility function through autonomous vehicles that are capable of making individually rational decisions to optimize their own utility functions. The first important aspect of the problem is to choose the utility functions of the vehicles in such a way that the objectives of the vehicles are localized to each vehicle yet aligned with a global utility function. The second important aspect of the problem is to equip the vehicles with an appropriate negotiation mechanism by which each vehicle pursues the optimization of its own utility function. We present several design procedures and accompanying caveats for vehicle utility design. We present two new negotiation mechanisms, namely, “generalized regret monitoring with fading memory and inertia” and “selective spatial adaptive play,” and provide accompanying proofs of their convergence. Finally, we present simulations that illustrate how vehicle negotiations can consistently lead to near-optimal assignments provided that the utilities of the vehicles are designed appropriately.

The Use of Utility Functions for Investment Channel Choice in Defined Contribution Retirement Funds. II: A Proposed System

2003 ◽  
Vol 9 (4) ◽  
pp. 903-958 ◽  
Author(s):  
R. J. Thomson
Keyword(s):  
Stochastic Model ◽  
Utility Function ◽  
Stochastic Models ◽  
Utility Functions ◽  
Retirement Income ◽  
Defined Contribution ◽  
Channel Choice ◽  
Retirement Funds

ABSTRACTIn this paper a system for recommending investment channel choices to members of defined contribution retirement funds is proposed. The system is interactive, using a member's answers to a series of questions to derive a utility function. The observed values are interpolated by means of appropriate formulae to produce a smooth utility function over the whole positive range of benefits at retirement. The resulting function, together with stochastic models of the returns on the available channels and of the annuity factor at exit, is then used to recommend an optimum apportionment of the member's investment. The proposed system is applied to the observed values of utility functions of post-retirement income elicited from members of retirement funds. Difficulties in the application are discussed and the results are analysed. The sensitivity of the recommendations to the parameters of the stochastic model is discussed.

scholarly journals A Utility Function for Examining Policy Affecting Salmon on the Skeena River

1977 ◽  
Vol 34 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Ralph L. Keeney
Keyword(s):  
Interest Groups ◽  
Utility Function ◽  
Utility Functions ◽  
Multiple Objectives ◽  
Utility Model ◽  
Multiattribute Utility ◽  
Utility Models ◽  
Constructive Discussion ◽  
Skeena River ◽  
Comparative Examination

The interests of many groups, some with multiple objectives, are important to include in evaluating strategies affecting salmon in the Skeena River. A multiattribute utility model is proposed for addressing these issues. Two first-cut utility functions are assessed using the preferences of two individuals familiar with the problem. These utility functions provide a basis for constructive discussion to arrive at a reasonable utility function for examining alternative policies. Two unique features of this study are the explicit focus on value tradeoffs and equity considerations among interest groups, and a comparative examination of the two first-cut multiattribute utility models. This examination indicates the range of fundamental preferences which can be captured using multiattribute utility functions and illustrates the potential of the theory for conflict illumination and resolution.

scholarly journals Design of utility functions for game-based channel allocation in cognitive radio wireless sensor network

2021 ◽  
Vol 22 (2) ◽  
pp. 1013
Author(s):  
Prativa Rai ◽  
Mrinal Kanti Ghose ◽  
Hiren Kumar Deva Sarma
Keyword(s):  
Game Theory ◽  
Cognitive Radio ◽  
Wireless Sensor Network ◽  
Utility Function ◽  
Sensor Network ◽  
Channel Allocation ◽  
High Growth ◽  
Utility Functions ◽  
Wireless Sensor ◽  
Allocation Algorithm

Cognitive radio enabled wireless sensor network is capable of reducing the spectrum scarcity problem of the wireless networks. Looking at the scarcity of available bandwidth, and the high growth in the number of communication devices in recent times, cognitive radio technology has proven to be a promising technology for the days to come. The application of Game Theory in cognitive radio networks has been visible in recent research works. However, only limited literature is available in which possibilities of applying the game-theory based approaches for the challenging task of channel assignment in cognitive radio wireless sensor are available in the literature. It is understood that the crux of the solution to the problem of scheming games for allocation of the channel is centered on the selection of the utility function in order to increase the efficiency of the channel allocation algorithm. Accordingly, the study regarding the influence of several utility functions on the performance of the corresponding channel allocation algorithm is important.  Such a study enables designers to arrive at the optimal utility function to be used in game-theory based channel allocation algorithms, and the same is explored to the best extent, in this paper. The detailed procedure of allocating channels to all the contending nodes through game-based channel allocation has been discussed in this paper. Moreover, the performance of six different utility functions proposed which can be used for channel allocation using game theory has been evaluated for respective performances through MATLAB-based simulations.

scholarly journals Revisiting the Approximation Bound for Stochastic Submodular Cover

2018 ◽  
Vol 63 ◽  
pp. 265-279
Author(s):  
Lisa Hellerstein ◽  
Devorah Kletenik
Keyword(s):  
Utility Function ◽  
Utility Functions ◽  
The State ◽  
Single Item ◽  
Set Size ◽  
Approximation Bound ◽  
Maximum Utility ◽  
Independent States ◽  
Stochastic Setting ◽  
Expected Increase

Deshpande et al. presented a k(ln R + 1) approximation bound for Stochastic Submodular Cover, where k is the state set size, R is the maximum utility of a single item, and the utility function is integer-valued. This bound is similar to the ln Q/(eta+1) bound given by Golovin and Krause, whose analysis was recently found to have an error. Here Q >= R is the goal utility and eta is the minimum gap between Q and any attainable utility Q' < Q. We revisit the proof of the k(ln R + 1) bound of Deshpande et al., fill in the details of the proof of a key lemma, and prove two bounds for real-valued utility functions: k(ln R_1 + 1) and (ln R_E + 1). Here R_1 equals the maximum ratio between the largest increase in utility attainable from a single item, and the smallest non-zero increase attainable from that same item (in the same state). The quantity R_E equals the maximum ratio between the largest expected increase in utility from a single item, and the smallest non-zero expected increase in utility from that same item. Our bounds apply only to the stochastic setting with independent states.

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