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This paper presents a novel optimization model that explores the optimal investment strategies for DC pension plans with return of premium clauses. We have assumed that the financial market consists of a risk-free asset and a risky asset, where the price of the risky asset follows the CEV model. Under the expected utility criterion, the optimal investment strategies were derived by employing stochastic optimal control theory and the Legendre transformation method. Explicit expressions of the optimal investment strategy were provided when the utility function was specified as exponential, power, or logarithmic. Finally, numerical analysis was conducted to examine the impact of factors such as interest rate, return rate, and volatility of the risky asset on the optimal strategies.

Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivatives of expected utility for various utility functions, significantly broadening the potential for analytical and computational applications. We apply these theoretical advancements to a case study involving the optimal production strategies of competitive firms, demonstrating the practical implications of our findings in economic decision making.

We prove the existence of an expected utility function for preferences over probabilistic prospects satisfying Strict Monotonicity, Indifference, the Common Ratio Property, Substitution and Reducibility of Extreme Prospects. The example in [1] that is inconsistent with the existence of a von Neumann-Morgenstern for preferences over probabilistic prospects, violates the Common Ratio Property. Subsequently, we prove the existence of expected utility functions with piecewise linear Bernoulli utility functions for preferences that are piece-wise linear. For this case a weaker version of the Indifference Assumption that is used in the earlier existence theorems is sufficient. We also state analogous results for probabilistic lotteries. We do not require any compound prospects or mixture spaces to prove any of our results. In the third last section of this paper, we “argue” that the observations related to Allais paradox, do not constitute a violation of expected utility maximization by individuals, but is a likely manifestation of individuals assigning (experiment or menu-dependent?) subjective probabilities to events which disagree with their objective probabilities.

In the classical newsvendor problem, it is considered that the newsvendor is risk-neutral and the Optimal Ordering Quantity (OOQ) was found which maximizes the newsvendor expected profit. In the real world, different investors have different attitudes towards risk. Accordingly, this paper considers the utility function which is commonly being used to model the attitude of the investor who makes the investment decision to maximize his expected utility instead of expected profit. This study considers the quadratic utility function and demonstrates that it can be used to describe the riskaverse as well as a risk-neutral investor with some conditions. Finally, by considering the risk profile of the investor at different levels of investor initial wealth, we have developed a method to determine the OOQ which maximizes the expected utility. From the numerical examples, it is clear that the OOQ and hence the expected utility varies depending on an investor’s attitude toward the risk and investment decision. At different levels of initial wealth, the attitudes toward the risk of the investors are different and the sensitivity analysis demonstrates how an investor can choose the initial wealth and OOQ to maximize his expected utility.

The notion of utility gained a strong foothold in health economics over the last decades. However, the concept of health utility has not yet been decisively or irrefutably defined and the definitions that exist often do not take into account the current state of psychological literature. This perspective paper shows that the current definition of health utility emphasizes decision-making processes, deploys personal preferences, assumes psychological egoism, and attempts to objectively and cardinally measure utility. However, these foundational axioms that underly the current definition of health utility are not necessarily in concurrence with the current state of psychological literature. Due to these perceived shortcomings of the current health utility definition, it may be beneficial to redefine the concept of health utility in accordance with the current state of psychological literature. In order to develop such a revised definition of health utility the commonly deployed formula (Eidos = Genos + Diaphora) originating from Aristotle’s metaphysics is applied. The revised definition of health utility proposed in this perspective paper alludes to health utility as ‘the subjective value, expressed in terms of perceived pain or pleasure, that is attributed to the cognitive, affective and conative experience of one’s own physical, mental and social health state, which is determined through self-reflection and interaction with significant others’. Although this revised definition does neither replace nor supersede other conceptualizations of health utility, it may serve as a refreshing avenue for further discussion and could, eventually, support policymakers and health economists in operationalizing and measuring health utility in an even more accurate and veracious manner.

This study investigates ambiguity aversion within the framework of a utility-maximizing investor under a modified constant-elasticity-of-volatility (M-CEV) model for the underlying asset. We derive closed-form solutions of a non-affine type for the optimal allocation and value function via a Cauchy problem. This work generalizes previous results in non-ambiguous settings by extending existing work to Hyperbolic Absolute Risk Aversion utility (HARA), correcting some typos in the literature for Constant Relative Risk Aversion utility (CRRA). Helpful details and derivations are also included in the manuscript.

Abstract The Income Stabilization Tool, a risk management scheme introduced within the Common Agricultural Policy (CAP) 2014–2020, could help European Union farmers manage the income risks they face. This study assesses the potential impact of implementing this tool through the maximum level of contribution to the fund which determines an indifference to participate in the fund and its financial sustainability. The study relies on an expected utility approach and assesses the variability of loss ratios over time using a sample of Italian hazelnut farms as a case study. The participation depends on the level of farmers' contributions and their degree of risk aversion. However, the CAP public support makes the scheme financially sustainable.

Since the beginning of the 21st century, a new interdisciplinary research movement has started, which aims at developing quantum math-like (or simply quantum-like) models to provide an explanation for a variety of socio-economic processes and human behaviour. By making use of mainly the probabilistic aspects of quantum theory, this research movement has led to many important results in the areas of decision-making and finance. In this article, we introduce a novel and more exhaustive approach, to analyze the socio-economic processes and activities, than the pure quantum math-like modelling approach, by taking into account the physical foundations of quantum theory. We also provide a plausibility argument for its exhaustiveness in terms of what we can expect from such an approach, when it is applied to, for example, a generic socio-economic decision process.

Conditionally automated driving is expected to become available to the public in near future. However, the driver is expected to take over control from the system when a sudden system failure occurs or when the driving automation system is approaching its operational design domain (ODD) exit. The time budget for drivers to take over control is different: (1) in the sudden system failure situation, the system needs to be deactivated immediately with issuing a request to intervene (RTI); (2) in the ODD exit situation, the system could keep active for a while after the issue of RTI. In this paper, two hypotheses were put forward based on driver’s expected utility analysis: (1) the drivers respond faster to the RTI in the system failure situation; (2) the drivers perform smoother driving behaviour after takeover in the ODD exit situation. We recruited 32 participants to conduct a driving simulator experiment, in which the driver takeover performance in system failure situation was compared with that in the ODD exit condition. Results revealed that drivers responded significantly faster in the system failure situation. There was no significant difference of longitudinal takeover performance between the two types of limitations. However, the drivers generally performed smoother lateral takeover performance in the ODD exit situation, which supported our hypothesis.

We analyse the problem of finding the optimal combination of quota-share and stop loss treaties, maximizing the expected utility or the adjustment coefficient of the cedent, for each of two risks dependent through a copula structure. By risk we mean a line of business or a portfolio of policies. Results are obtained numerically, using the software Mathematica. Sensitivity of the optimal reinsurance strategy to several factors are investigated, including: i) the dependence level, by means of the Kendall’s tau and the dependence parameter; ii) the type of dependence, using different copulas describing different tail behaviour; iii) the reinsurance calculation principles, where expected value, variance and standard deviation principles are considered. Results show that different dependence structures, yield significantly different optimal solutions. The optimal treaty is also very sensible to the reinsurance premium calculation principle. Namely, for variance related premiums the optimal solution is not the pure stop loss. In general, the maximum adjustment coefficient decreases when dependence increases.

Probabilistic decision-making is a fundamental problem considered in many disciplines from engineering to social sciences. In this article, we address decision-making in contexts where the law of large numbers (LLN) does not apply. Non-LLN regimes include almost all high-impact decisions. The rise of artificial intelligence (AI) decision making is further increasing the importance of developing principled approaches for such problems. In this regard, we first introduce a method called bounded expectation (BE) to apply the accepted principle of ignoring negligible probabilities. We show that BE provides some satisfactory results and insights into some decision-making problems. Pointing out some shortcomings of BE, we then turn to a much more general setting, using change-of-probability measures. We show that the proposed approach can be considered a generalization of expected utility theory (EUT) from two different perspectives. First, the approach converges to EUT as the number of repetitions grows. Additionally, when the fundamental distortion parameter, $\epsilon $ , is set to zero, the proposed theory reduces to EUT. We then propose a systematic approach to applying the developed framework to non-LLN decisions. Finally, through a real-world example, we compare the decisions made with the proposed method and the conventional methods. It is speculated that due to the complexity and multidimensionality of decision-making under non-LLN regimes, the presented ideas can potentially lead to considerable further research, some of which is discussed in this article.

We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output’s distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output’s distribution and satisfies the stress. We further derive the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples and which is implemented in the R package SWIM on CRAN. The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models.

A challenging clinical conundrum arises in severe traumatic brain injury patients who develop intractable intracranial hypertension. For these patients, high morbidity interventions such as surgical decompression and barbiturate coma have to be considered against a backdrop of uncertain outcomes including prolonged states of disordered consciousness and severe disability. The clinical evidence available to guide shared decision-making is mainly limited to one randomized controlled trial, the RESCUEicp. However, since the publication of this trial significant controversy has been ongoing over the interpretation of the results. Is the mortality benefit from surgery merely a trade off for unacceptable long-term disability? How should treatment options, possible outcomes, and results from the trial be communicated to surrogates? How do we incorporate patient values into forming plans of care? The aim of this article is to sketch an approach based on insights from Decision Theory, and specifically deciding under uncertainty. The mainstream normative decision theory, Expected Utility (EU) theory, essentially says that, in situations of uncertainty, one should prefer the option with greatest expected desirability or value. The steps required to compute expected utilities include listing the possible outcomes of available interventions, assigning each outcome a utility ranking representing an individual patient's preferences, and a conditional probability given each intervention. This is a conceptual framework meant to supplement, and enhance shared decision making by assuring that patient values are elicited and incorporated, the possible range and nature of outcomes is discussed, and finally by attempting to connect best available means to patient-individualized ends.

We present a novel characterization of random rank-dependent expected utility for finite datasets and finite prizes. As a byproduct, we obtain a characterization of random expected utility that works for finite datasets. The test lends itself to statistical testing. We apply our test to an experimental dataset and find evidence against random expected utility, while random rank-dependent expected utility can explain the dataset.

A new theory of decision-making under risk, the Opportunity-Threat Theory is proposed. Analysis of risk into opportunity and threat components allows description of behavior as a combination of opportunity seeking and threat aversion. Expected utility is a special case of this model. The final evaluation is an integration of the impacts of opportunity and threat with this expectation. The model can account for basic results as well as several ``new paradoxes'' that refuted cumulative prospect theory in favor of configural weight models. The discussion notes similarities and differences of this model to the configural weight TAX model, which can also account for the new paradoxes.

Servo presses enable new types of forming motion profiles that can be used to stamp difficult materials, such as high strength steels. This paper presents an application of Bayesian statistics to intelligently select which motion profile maximizes the expected utility given the properties of the incoming material. Bayesian logistic regression was used in conjunction with expected utility to estimate manufacturing returns, which can be used to make informed process decisions. A use case is presented, which demonstrates that the Smart Forming Algorithm can increase expected returns by more than 20%.

A literature review of the important trends in predictive crime modeling and the existing measures of accuracy was undertaken. It highlighted the need for a robust, comprehensive and independent evaluation and the need to include complementary measures for a more complete assessment. We develop a new measure called the penalized predictive accuracy index (PPAI), propose the use of the expected utility function to combine multiple measures and the use of the average logarithmic score, which measures accuracy differently than existing measures. The measures are illustrated using hypothetical examples. We illustrate how PPAI could identify the best model for a given problem, as well as how the expected utility measure can be used to combine different measures in a way that is the most appropriate for the problem at hand. It is important to develop measures that empower the practitioner with the ability to input the choices and preferences that are most appropriate for the problem at hand and to combine multiple measures. The measures proposed here go some way towards providing this ability. Further development along these lines is needed.

Orientation: The expected utility theory was used as the lens to focus on how South African investors’ demographics are associated with their financial risk tolerance. Research purpose: To determine the association between eight demographic variables of investors and their financial risk tolerance scores. Motivation for the study: Instead of applying a single aggregated risk tolerance score, this study distinguishes itself from most other studies to refine risk tolerance into various factors. Research approach/design and method: Data for this study were obtained from FinaMetrica Pty Ltd, namely the results of their 25-item risk tolerance questionnaire, which also includes eight demographic variables of the respondents. In total, 3473 of the respondents were considered valuable. This study identified three risk tolerance factors: factor 1 (level of risk), factor 2 (past experience) and factor 3 (personal feelings and attitudes). Main findings: The study found that men are statistically significantly more risk tolerant than women, and investors’ education level, income level and combined income are all positively statistically significantly associated with risk tolerance as per factors 1, 2 and 3. Some unique findings were revealed from this study, that is, as investors age, their risk tolerance increases (as per factor 1); married investors are more risk tolerant than the unmarried (as per factor 2); and the number of dependents is positively associated with risk tolerance, factors 1 and 2. Practical/managerial implications: The practical value of the study is that investors and financial advisors have a more enhanced view to better understand the degree of association of demographic variables on investors’ level of risk; the roles of past experiences and personal attitudes and feelings are associated with demographic variables. Contribution/value-add: Breaking up the risk tolerance into factors contributes to a more refined analysis being conducted.

Adolescence is a period of life during which peers play a pivotal role in decision-making. The narrative of social influence during adolescence often revolves around risky and maladaptive decisions, like driving under the influence, and using illegal substances (Steinberg, 2005). However, research has also shown that social influence can lead to increased prosocial behaviors (Van Hoorn et al., 2017) and a reduction in risk-taking (Braams et al., 2019). While many studies support the notion that adolescents are more sensitive to peer influence than children or adults, the developmental processes that underlie this sensitivity remain poorly understood. We argue that one important reason for this lack of understanding is the absence of precisely formulated models. To make a first step toward formal models of social influence during adolescence, we first identify three prominent verbal models of social influence in the literature: (1) social motivation, (2) reward sensitivity, and (3) distraction. We then illustrate how these can be translated into formal models, and how such formal models can inform experimental design and help identify developmental processes. Finally, by applying our formal models to existing datasets, we demonstrate the usefulness of formalization by synthesizing different studies with seemingly disparate results. We conclude with a discussion on how formal modeling can be utilized to better investigate the development of peer influence in adolescence.

We try to analyze the attitudes of taxpayers regarding tax system. We propose an approach which combines between theory and practice. In the first step, we present a normative approach; based on a theoretical study. In order to produce an empirical knowledge of an aspect of reality we present a descriptive approach; based on a survey of different categories of taxpayers. We have released some conditions of the model of expected utility and we have introduced a new parameter reflecting the efficiency of tax control. Previous studies are based on a maximal efficiency tax control (100%) which is a particular case of our model. We found that for Moroccan case, the fraud is related to the size of companies; it’s more important in the big companies than small and medium enterprises. The findings will help tax authorities to achieve their goals in fighting tax fraud.

Risk aversion is an important research area in the field of agricultural economics in the last years. Creating effective and efficient risk management tools in an increasingly volatile economic and natural environment requires proper recognition of farmers’ behavior and attitudes towards risk. In this context, the main aim of the paper was to estimate farmers’ attitudes towards risk and identification of farm’s and farmer’s characteristics in dependency on risk aversion level. The assessment of farmers’ preferences towards risk was based on hypothetical games in a representative sample of 600 Polish farms—participants of Farm Accountancy Data Network (FADN). Based on the interviews with farmers, a relative risk aversion coefficient has been estimated. Results revealed that on average Polish farmers have quite a strong risk aversion. Their attitudes towards risk are strongly linked with their self-assessment regarding their way of making decisions under risk. Some relations between farmers’ risk aversion and perception of selected risk factors could also be observed. The results revealed that the application of specified risk management tools by farmers and their potential reaction to a significant income drop are related to risk aversion level.

The expected utility principle is often used to compute the insurance premium through a second-order approximation of the expected value of the utility of losses. We investigate the impact of using a more accurate approximation based on the fourth-order statistics of the expected loss and derive the premium under this expectedly more accurate approximation. The comparison between the two approximation levels shows that the second-order-based premium is always lower (i.e., an underestimate of the correct one) for the commonest loss distributions encountered in insurance. The comparison is also carried out for real cases, considering the loss parameters values estimated in the literature. The increased risk of the insurer is assessed through the Value-at-Risk.

Information technologies supporting online dating create an enormous expansion of the population of potential partners. However, such a large population offers too much information, too many choices, too many potential (and potentially unsatisfying) partners. The importance of conducting a search efficiently is imperative. This paper applies the economic theory of decision-making under uncertainty to analyze the optimizing behavior of rational adults who use internet dating sites. The analysis applies expected utility to a randomly distributed population of candidates. The main result derives conditions that must be satisfied by a searcher who allocates his time efficiently. The result is based, in part, on an assumption that the behavior of the searcher is guided by his recognition of adverse selection and “cheap talk.” The analytical results can be applied to search-and-action scenarios totally unrelated to a search for a romantic partner. These include such diverse scenarios as the deployment of military drones searching for a target or a search for a home or a search for an employment opportunity.

Two basic trade-offs interact while our brain decides how to move our body. First, with the cost-benefit trade-off, the brain trades between the importance of moving faster toward a target that is more rewarding and the increased muscular cost resulting from a faster movement. Second, with the speed-accuracy trade-off, the brain trades between how accurate the movement needs to be and the time it takes to achieve such accuracy. So far, these two trade-offs have been well studied in isolation, despite their obvious interdependence. To overcome this limitation, we propose a new model that is able to simultaneously account for both trade-offs. The model assumes that the central nervous system maximizes the expected utility resulting from the potential reward and the cost over the repetition of many movements, taking into account the probability to miss the target. The resulting model is able to account for both the speed-accuracy and the cost-benefit trade-offs. To validate the proposed hypothesis, we confront the properties of the computational model to data from an experimental study where subjects have to reach for targets by performing arm movements in a horizontal plane. The results qualitatively show that the proposed model successfully accounts for both cost-benefit and speed-accuracy trade-offs.

Research of rationality represents area of interaction between philosophy and economics. Development of neuroscience and advances in scientific research lead to another common characteristic – gradual loss of competence to comment on results of scientific research. This article describes development of the concept of economic rationality and makes an effort to deal with the following question: Can philosophy play useful role in contemporary discussions about the nature of decision-making and rationality? The authors conclude that philosophy itself does not have any extraordinary method which is able to ensure an exclusive position within the contemporary research. The role of philosophy consists in analyzing arguments, explanations of terms, in the search for ethical consequences, or in the preparation of social sciences to naturalization through critical evaluation of obsolete approaches that are inconsistent with the findings of natural science.

The assumption about travellers’ route choice behaviour has major influence on the traffic flow equilibrium analysis. Previous studies about the travellers’ route choice were mainly based on the expected utility maximization theory. However, with the gradually increasing knowledge about the uncertainty of the transportation system, the researchers have realized that there is much constraint in expected util­ity maximization theory, because expected utility maximiza­tion requires travellers to be ‘absolutely rational’; but in fact, travellers are not truly ‘absolutely rational’. The anticipated regret theory proposes an alternative framework to the tra­ditional risk-taking in route choice behaviour which might be more scientific and reasonable. We have applied the antici­pated regret theory to the analysis of the risk route choosing process, and constructed an anticipated regret utility func­tion. By a simple case which includes two parallel routes, the route choosing results influenced by the risk aversion degree, regret degree and the environment risk degree have been analyzed. Moreover, the user equilibrium model based on the anticipated regret theory has been established. The equivalence and the uniqueness of the model are proved; an efficacious algorithm is also proposed to solve the model. Both the model and the algorithm are demonstrated in a real network. By an experiment, the model results and the real data have been compared. It was found that the model re­sults can be similar to the real data if a proper regret degree parameter is selected. This illustrates that the model can better explain the risk route choosing behaviour. Moreover, it was also found that the traveller’ regret degree increases when the environment becomes more and more risky.

The body of literature on the relationship between risk aversion and wealth is extensive. However, little attention has been given to examining how future realizations of wealth might affect (current) risk decisions. Using paired lottery choice experiments and exposing subjects experimentally to imagined future wealth frames, I find that individuals are more risk-seeking if they are asked to imagine that they will be wealthy in the future. Yet I find that individuals are not significantly more risk-averse if they are asked to imagine that they will be poor in the future. I discuss theoretical and policy implications of these findings, including why savings rates are so low in the United States.

Bridges create transport infrastructure and are subjected to long term witness of economic design, reliability, durability, maintainability and external risk (natural and human hazards). Deficient design of bridges points to high investment costs, low quality, retrofits maintenance costs, mitigates quality damages. The primary reason of the problem is usually stated high investment costs. Resources for investment are limited over and over again. However, approach for evaluating and comparing the cost effectiveness in practical design does not dominate in present-days as arbitration of different strategies and warrant for avoiding critical economic or functional situations. This paper illustrates a method for estimating the retrofits for bridges design based on Life-Cycle Costs and Cost-Benefit Analysis. The approach integrates cost model, fragility of as-designed and retrofitted benefits for a range of externalities and associated potential changes in design and economical retrofit. The emphasis on life-time performance and benefits, as opposed to initial retrofit acquisition investment cost alone, paves the way to risk-wise investment and also helps to support upgrade actions for sustainable infrastructure. An application of the holistic approach Life-Cycle Cost and benefit analysis is conducted for two representative bridges of highway class. The available financing has a big influence on the chosen technical design.

Economic agents are constantly making decisions to maximize their expected utilities while accepting some risks. The question is that, how the efficient portfolio of the assets in a specific level of risk is formed to maximize the individual’s utility? To model the behavior of decision maker, economists and financial mathematicians consider both the variables which affect individual’s decision making behaviour, and the methods to include real world uncertainties. In the model presented in this paper the decision maker chooses between two types of assets: risky and risk-free. The returns on these two types are different and the utility of the decision maker is a function of his wealth (consisting of his initial wealth and the returns of his portfolio of the assets). The comparison between the results of financial modeling with decision-making theories under uncertainty within microeconomic literature reveals the impacts of three factors: degree of risk-aversion, relative return of assets and volatility of risky assets prices, on optimum portfolio allocation.

In this paper we analyze insurance demand when the utility function depends both upon final wealth and the level of losses or gains relative to a reference point. Besides some comparative statics results, we discuss the links with first-order risk aversion, with the Omega measure, and with a tendency to over-insure modest risks that has been been extensively documented in real insurance markets.

Under state-dependent preferences, probabilities and units of scale of state-dependent utilities are not separately identified. In standard models, only their products matter to decisions. Separate identification has been studied under implicit actions by Drèze or under explicit actions and observations by Karni. This paper complements both approaches and relates them when conditional preferences for final outcomes are independent of actions and observations. That special case permits drastic technical simplification while remaining open to some natural extensions.

Humans and other animals are idiosyncratically sensitive to risk, either preferring or avoiding options having the same value but differing in uncertainty. Many explanations for risk sensitivity rely on the non-linear shape of a hypothesized utility curve. Because such models do not place any importance on uncertainty per se, utility curve-based accounts predict indifference between risky and riskless options that offer the same distribution of rewards. Here we show that monkeys strongly prefer uncertain gambles to alternating rewards with the same payoffs, demonstrating that uncertainty itself contributes to the appeal of risky options. Based on prior observations, we hypothesized that the appeal of the risky option is enhanced by the salience of the potential jackpot. To test this, we subtly manipulated payoffs in a second gambling task. We found that monkeys are more sensitive to small changes in the size of the large reward than to equivalent changes in the size of the small reward, indicating that they attend preferentially to the jackpots. Together, these results challenge utility curve-based accounts of risk sensitivity, and suggest that psychological factors, such as outcome salience and uncertainty itself, contribute to risky decision-making.

We review a decision theoretic, i.e., utility-based, motivation for entropy and Kullback-Leibler relative entropy, the natural generalizations that follow, and various properties of thesegeneralized quantities. We then consider these generalized quantities in an easily interpreted spe-cial case. We show that the resulting quantities, share many of the properties of entropy andrelative entropy, such as the data processing inequality and the second law of thermodynamics.We formulate an important statistical learning problem – probability estimation – in terms of ageneralized relative entropy. The solution of this problem reflects general risk preferences via theutility function; moreover, the solution is optimal in a sense of robust absolute performance.

This article hopes to facilitate dialogue within political studies by presenting a general description of the conceptual structure of Rational Choice Theory, RCT. The main goal is to present precisely and concretely the basic precepts of RCT both on its original formulation and on the adjustments that have been adopted in reply to internal and external critiques. Thus, it presents a brief discussion about how the acknowledgement of the theory’s limitations has guided some aspects of its recent evolution. The article concludes with some brief reflections on the usefulness of RCT in political science, not aimed at discussing the applicability of RCT in the social sciences, a discussion beyond the scope of the paper, but to initiate a discussion about how RCT constitutes itself as a theoretical framework that facilitates dialogue within the discipline.

This paper examines the use of market consultants and market information systems by grain and cotton producers. A model of producer demand for marketing information and consultants is proposed that decomposes price received into exogenous and endogenous components. The analysis is based on a survey of over 1,600 producers. The results suggest that expenditures on market information systems and market consultants are not independent and, more specifically, expenditures on marketing consultants substitute for expenditures on market information systems.

Prior to the 2000 planting season, some industry observers predicted acreage of genetically modified crops would decline dramatically. However, actual 2000 plantings presented a puzzle. Farmers reduced their acreage of genetically modified corn, but concurrently increased their acreage of genetically modified soybeans. We demonstrate that it may be theoretically optimal for risk-averse farmers to reduce their corn acreage but not their soybean acreage. However, past experience, attitudes, and farm size explained planting decisions to a larger degree than did risk preferences.

Drawing on empirical observations of operation of public distribution system in different states of India, the paper constructs a preliminary game theoretic model. It argues that an effective public distribution must be as universal as possible, delivery mechanism of fair price shops should be re-formed, they should be make them commercially viable and that special attention should be paid to PDS at times of high food inflation.

In this paper, we approximate the aggregate claims process by using the translated gamma process under the classical risk model assumptions, and we investigate the ultimate ruin probability. We consider optimal reinsurance under the minimum ultimate ruin probability, as well as the maximum benefit criteria: released capital, expected profit and exponential-fractional-logarithmic utility from the insurer’s point of view. Numerical examples are presented to explain how the optimal initial surplus and retention level are changed according to the individual claim amounts, loading factors and weights of the criteria. In the decision making process, we use The Analytical Hierarchy Process (AHP) and The Technique for Order of Preference by Similarity to ideal Solution (TOPSIS) methods as the Multi-Attribute Decision Making methods (MADM) and compare our results considering different combinations of loading factors for both exponential and Pareto individual claims.

Estimation risk occurs when parameters relevant for decision making are uncertain. Bayes' criterion is consistent with expected-utility maximization in the presence of estimation risk. This article examines optimal (Bayes') land allocations and land allocations obtained using the traditional plug-in approach and two alternative decision rules. Bayes' allocations are much better economically than the other allocations when there are few sample observations relative to activities. Calculation of certainty equivalent returns (CERs) with estimation risk is also discussed and illustrated. CERs are typically (and incorrectly) calculated with the plug-in approach. Plug-in CERs may be extremely misleading.

This article reviews two major approached used in the past for risk analysis - the expected utility approach and the use of safety rules and endeavors to reconcile their applicability and use in light of the recent nonexpected utility risk literature and working using the mean-Gini coefficient for risk analysis. This leads to the identification of several "reduced form" hypotheses that hold under a variety of theoretical structures and to a discussion of some empirical evidence regarding these hypotheses. The major lesson of recent research of individual behavior under risk is that it is not always consistent with the expected utility approach; in short, there is no generic model for evaluating behavior under risk.