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We study random joint choice rules, allowing for interdependence of choice across agents. These capture random choice by multiple agents, or a single agent across goods or time periods. Our interest is in separable choice rules, where each agent can be thought of as acting independently of the other. A random joint choice rule satisfies marginality if for every individual choice set, we can determine the individual's choice probabilities over alternatives independently of the other individual's choice set. We offer two characterizations of random joint choice rules satisfying marginality in terms of separable choice rules. While marginality is a necessary condition for separability, we show that it fails to be sufficient. We provide an additional condition on the marginal choice rules which, along with marginality, is sufficient for separability. [ABSTRACT FROM AUTHOR]

This paper introduces a new shifted negative log‐normal distribution for the price parameter in mixed multinomial logit models. The new distribution, labeled as the μ‐shifted negative log‐normal distribution, has desirable properties for welfare analysis and in particular a point mass that is further away from zero than the negative log‐normal distribution. This contributes to mitigating the "exploding" implicit prices issue commonly found when the price parameter is specified as negative log‐normal and the model is in preference space. The new distribution is tested on five stated preference datasets. Comparisons are made with standard alternative approaches such as the willingness‐to‐pay (WTP) space approach. It is found that the μ‐shifted distribution yields substantially lower mean marginal WTP estimates compared to the negative log‐normal specification and similar to the values derived from models estimated in WTP‐space with flexible distributions, while at the same time fitting the data as well as the negative log‐normal specification. [ABSTRACT FROM AUTHOR]

This paper considers the estimation of random regret minimization models using rank-ordered choice data. By analyzing Monte Carlo simulations results, we find that the efficiency increases as we use additional information on the ranking. Compared with the multinomial logit model with utility maximization, the simulation results show that the standard random regret minimization model is slightly worse than the multinomial logit model based on both the mean bias and root mean squared error of the estimator of the model parameter β . When using long ranking choice data to estimate the random regret minimization model, based on the mean bias and root mean squared error of the estimator, we find that the rank-ordered random regret minimization model has advantages over the multinomial logit model and the standard random regret minimization model. Analysis of real data shows that our method is very effective in estimating model parameters. [ABSTRACT FROM AUTHOR]

Barseghyan and Molinari give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but context-independent. In this comment, we build on their insights and present identification results in a setting where the risk preferences are allowed to be context-dependent. [ABSTRACT FROM AUTHOR]

The northern white-breasted hedgehog (Erinaceus roumanicus) is a charismatic insectivore mammal threatened by habitat loss, the use of biocides, and collisions with vehicles. We aimed at valorizing hedgehog conservation through the contingent valuation method for estimating the proportion and the amount of willingness to pay (WTP) and the effects of cognitions and sociodemographic characteristics on WTP. We collected data through interviews with 704 residents of four administrative regions of northern Greece. Binary logistic regression and Welsh–Poe interval regression were used for estimating the effects of predictors on the proportion and mean WTP, respectively. Most participants (58.2%) were WTP a mean of EUR 31.7 for hedgehog conservation, totaling EUR 21.9 million annually when projected to the population. Positive attitudes, mutualism wildlife orientations, intention to participate in hedgehog conservation actions, and participation in consumptive and non-consumptive wildlife-related recreational activities increased WTP. Highly educated females, with high income and a pet were more WTP than less educated males, with low income and without a pet. Findings provided necessary information to managers about the value of hedgehog conservation, the potential for raising required funds and the differences in WTP among and within public groups. [ABSTRACT FROM AUTHOR]

Simple Summary: The Balkan chamois is an agile mammal of the rocky mountain slopes of the Balkan peninsula. In Greece, its southernmost limit, it exists in six population groups. Illegal hunting and the isolation of populations are the major threats to the species. Therefore, conservation actions, such as the control of illegal hunting and the creation of wildlife corridors are required for securing viable populations. We used an econometric model to estimate the willingness to pay (WTP) for chamois conservation. People from the region of Eastern Macedonia and Thrace participated in our survey. We asked them if they were WTP an annual tax for the next five years for implementing conservation actions for the chamois. Most of the surveyed people stated a considerable WTP that could yield adequate resources for funding relevant conservation actions. We also examined the effects of several factors on WTP. We found that increasing the knowledge about and improving the attitudes toward the species, targeting groups, such as males, those less educated, and who have not seen the species in the wild, through suitable education and outreach programs would increase public support and WTP for the species. Our findings would help successfully implement conservation plans for the chamois. The Balkan chamois (Rupicapra rupicapra balcanica) is a caprine of the rocky mountain slopes, threatened in Greece by illegal hunting and population isolation. We used a contingent valuation method to assess the willingness to pay (WTP) for chamois conservation of 500 residents of the region of Eastern Macedonia and Thrace. Most of the participants (61.6%) were WTP a mean of EUR 41.6 for chamois conservation, totaling EUR 6.03 million for the target population. Attitudes toward and knowledge about chamois, moralistic worldviews (spiritual reverence and ethical concern for nature and wildlife), participation in wildlife-related consumptive outdoor activities (i.e., hunting and fishing), intention to participate in conservation actions for the species, and encounters with the species in the wild were positively associated with WTP for its conservation. Dominionistic worldviews (humans have mastery, physical control, and dominance of wildlife) were negatively associated with WTP, while highly educated females with high income were more WTP for implementing relevant conservation actions. Factors involving previous knowledge of the chamois positively influenced the WTP, thus, confirming the construct's bias toward charismatic species. The findings show that Greek residents highly value the chamois and its conservation and would be useful for advising this process and achieving its conservation management. [ABSTRACT FROM AUTHOR]

This paper studies an individual's random choice behavior when the objects of choice are random variables from a continuum of states of nature to a continuum of consequences. A decision problem is a nonempty finite set of random variables. A random choice rule specifies the choice distribution for each decision problem. We assume that each time an individual makes a choice from a decision problem, she conforms to dual expected utility theory. Since this theory is characterized by a probability distortion on the unit interval, the randomness in the individual's choice behavior is due to the variation in her distortion. We first provide an axiomatic characterization for this random dual expected utility model, and then characterize comparative risk aversion within this model. [ABSTRACT FROM AUTHOR]

The random utility model (RUM, McFadden and Richter (1990)) has been the standard tool to describe the behavior of a population of decision makers. RUM assumes that decision makers behave as if they maximize a rational preference over a choice set. This assumption may fail when consideration of all alternatives is costly. We provide a theoretical and statistical framework that unifies well‐known models of random (limited) consideration and generalizes them to allow for preference heterogeneity. We apply this methodology in a novel stochastic choice data set that we collected in a large‐scale online experiment. Our data set is unique since it exhibits both choice set and (attention) frame variation. We run a statistical survival race between competing models of random consideration and RUM. We find that RUM cannot explain the population behavior. In contrast, we cannot reject the hypothesis that decision makers behave according to the logit attention model (Brady and Rehbeck (2016)). [ABSTRACT FROM AUTHOR]

The increasing importance of liability-driven investment strategies and the shift towards retirement products with lower guarantees and more performance participation provide challenges for the development of portfolio optimization frameworks which cover these aspects. To this end, we establish a general and flexible terminal surplus optimization framework in continuous time, allowing for dynamic investment strategies and stochastic liabilities, which can be linked to the performance of an index or the asset portfolio of the insurance company. Besides optimality results in a fairly general surplus optimization setting, we obtain closed-form solutions for the optimal investment strategy for various specific liability models, which include the cases of index-linked and performance-linked liabilities and liabilities which are completely or only partially hedgeable. We compare the results in numerical examples and study the impact of the performance participation, unhedgeable risk components, different ways of modeling the liabilities and the relative risk aversion parameter. We find that performance- or index-linked liabilities, which provide a close link between the wealth of the insurance company and its liabilities, allow for a higher allocation in the risky investment. On the other hand, unhedgeable risks reduce the allocation in the risky investment. We conclude that, aiming at a high expected return for the policy holder, insurance companies should try to connect the performance of insurance products closely to the wealth and minimize unhedgeable risks. [ABSTRACT FROM AUTHOR]

The econometrics of the Linear Probability Model (LPM) cast as binary choice random utility model and where probabilities are constrained in the [0,1] interval is unexplored. The paper fills this gap. Assumptions are identified under which constrained maximum likelihood estimators exist and are unique, consistent and asymptotically normal. A consistent estimator of the covariance matrix is provided. Statistics that can be used to evaluate the prediction validity of binary choice models are reviewed. With income independent choices, the LPM has the merit of closed-form welfare change measure for the sub-population of consumers shifting from one alternative to the other. Two datasets illustrate the theoretical insights. One from the Swiss Mobility and Transport Microcensus related to choices between teleworking and commuting, one from the German Socio-Economic Panel related to add-on health insurance subscription. The signs and statistical significance at 5% level of the coefficients are concordant across LPM, Logit and Probit. Model prioritization based on prediction validity is data specific and dependent on the statistics used. • Probabilities are linear in the coefficients and the attributes and are included in the interval between zero and one. • Distributions of the random components of the two alternatives generating the model are identified. • Conditions for the constrained maximum likelihood estimators to exist and be unique, consistent and asymptotically normal are provided. • Welfare measures with reference to sub-populations of shifters and non-shifters are in simple closed form. [ABSTRACT FROM AUTHOR]

The delay discounting perspective, which assumes analternative-wise processing of attribute information, has longdominated research on intertemporal choice. Recent studies,however, have suggested that intertemporal choice is based onattribute-wise comparison. This line of research culminated inthe tradeoff model (Scholten & Read, 2010; Scholten, Read,& Sanborn, 2014), which can accommodate most establishedbehavioral regularities in intertemporal choice. One drawbackof the tradeoff model, however, is that it is static, providingno account of the dynamic process leading to a choice. Herewe develop a dynamic tradeoff model that can qualitativelyaccount for empirical findings in intertemporal choiceregarding not only choices but also response times. Thedynamic model also outperforms the original, static tradeoffmodel when quantitatively fitting choices from representativedata sets, and even outperforms the best-performing dynamicmodel derived from Decision Field Theory in Dai andBusemeyer (2014) when fitting both choices and responsetimes.

The northern white-breasted hedgehog (Erinaceus roumanicus) is a charismatic insectivore mammal threatened by habitat loss, the use of biocides, and collisions with vehicles. We aimed at valorizing hedgehog conservation through the contingent valuation method for estimating the proportion and the amount of willingness to pay (WTP) and the effects of cognitions and sociodemographic characteristics on WTP. We collected data through interviews with 704 residents of four administrative regions of northern Greece. Binary logistic regression and Welsh–Poe interval regression were used for estimating the effects of predictors on the proportion and mean WTP, respectively. Most participants (58.2%) were WTP a mean of EUR 31.7 for hedgehog conservation, totaling EUR 21.9 million annually when projected to the population. Positive attitudes, mutualism wildlife orientations, intention to participate in hedgehog conservation actions, and participation in consumptive and non-consumptive wildlife-related recreational activities increased WTP. Highly educated females, with high income and a pet were more WTP than less educated males, with low income and without a pet. Findings provided necessary information to managers about the value of hedgehog conservation, the potential for raising required funds and the differences in WTP among and within public groups.

Among the reasons behind the choice behavior of an individual taking a stochastic form are her potential indifference or indecisiveness between certain alternatives, and/or her willingness to experiment in the sense of occasionally deviating from choosing a best alternative to give a try to other options. We introduce methods of identifying if and when a stochastic choice model may be thought of as arising due to any one of these three reasons. Each of these methods furnishes a natural way of making deterministic welfare comparisons within any model that is "rationalized" as such. In turn, we apply these methods, and characterize the associated welfare orderings, in the case of several well‐known classes of stochastic choice models. [ABSTRACT FROM AUTHOR]

We examine the effect of variability in model parameters onthe predictions of expected utility theory and cumulativeprospect theory, two of the most influential choice models indecision making research. We find that zero-mean andsymmetrically distributed noise in the underlying parametersof these models can systematically distort choiceprobabilities, leading to false conclusions. Likewise,differences in choice proportions across decision makersmight be due to differences in the amount of noise affectingunderlying parameters rather than to differences in actualparameter values. Our results suggest that care and cautionare needed when trying to infer the underlying preferences ofdecision makers, or the effects of psychological, biological,economic, and demographic variables on these preferences.

Improved seed propagation technologies (ISPTs) curb the shortage of quality seedlings for vegetable farmers. This study examines factors that influence the choice of improved vegetable seed propagation technologies in Arumeru and Mvomero districts, Tanzania for nursery beds and seedling trays. Descriptive statistics and a logit model are used to analyse data from 240 randomly sampled farmers. The results show that seedling trays are used by only 13% of all vegetable farmers, despite the technology's higher productivity and efficiency; and nursery beds are used by 87% of all farmers. The binary logit model results show that the choice of ISPTs is determined by access to seedling trays, credit, extension services and marital status. Thus, to increase the efficiency of transplant production and yield, the use of seedling trays for propagating vegetable seedlings should be promoted by increasing the availability of seedling trays and availing credit and extension services to vegetable farmers. [ABSTRACT FROM AUTHOR]

There are two ways to predict and evaluate decision-makers' route choice behavior: random utility maximization (RUM) and random regret minimization (RRM). In this paper, the main purpose is to use the characteristics of regret weight in GRRM to get a hybrid RUM-RRM model. To illustrate the asymmetry of RRM model, this paper uses a route choice case to interpret three main properties of RRM-based model: independence of irrelevant alternatives, semi-compensatory and compromise effect. Then the same scenario is used to interpret why and how the hybrid model can be obtained from the regret weight. What's more, the current empirical studies only used a stated preference survey to test and estimate the model. So GPS-based big data in Guangzhou is used to test the aforementioned models, which can get rid of the weakness of using the stated survey data. The result shows that although the RUM model outperforms most of the RRM models, using regret weight to get the hybrid model, it can also find better model fitness and coefficients consistent with our understanding of attributes. Finally, a value is used to help traffic designers choose a better position of U-turn on the road.

The Balkan chamois (Rupicapra rupicapra balcanica) is a caprine of the rocky mountain slopes, threatened in Greece by illegal hunting and population isolation. We used a contingent valuation method to assess the willingness to pay (WTP) for chamois conservation of 500 residents of the region of Eastern Macedonia and Thrace. Most of the participants (61.6%) were WTP a mean of EUR 41.6 for chamois conservation, totaling EUR 6.03 million for the target population. Attitudes toward and knowledge about chamois, moralistic worldviews (spiritual reverence and ethical concern for nature and wildlife), participation in wildlife-related consumptive outdoor activities (i.e., hunting and fishing), intention to participate in conservation actions for the species, and encounters with the species in the wild were positively associated with WTP for its conservation. Dominionistic worldviews (humans have mastery, physical control, and dominance of wildlife) were negatively associated with WTP, while highly educated females with high income were more WTP for implementing relevant conservation actions. Factors involving previous knowledge of the chamois positively influenced the WTP, thus, confirming the construct’s bias toward charismatic species. The findings show that Greek residents highly value the chamois and its conservation and would be useful for advising this process and achieving its conservation management.

Quick decision making under risk is ubiquitous in modern times, yet its consequences are not fully understood. Time pressure might change people's risk preferences, lead to less consistent choices, or change people's decision strategy. With the present work, we make the novel contribution of testing all hypotheses against each other in a unifying hierarchical Bayesian model. In two studies, participants decided repeatedly between two risky gambles either with or without high time pressure. We found a significant increase in risky choices under time pressure. With modeling, we show that time pressure decreased choice consistency but did not systematically affect people's risk preferences. In addition, the number of participants using simple, noncompensatory strategies increased slightly under time pressure. Finally, participants did not systematically choose easier gambles more often under time pressure. Thus, a reliable analysis of the effect of time pressure on preferential choice requires a model framework that allows for the distinction between the various effects time pressure can have. [ABSTRACT FROM AUTHOR]

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity. [ABSTRACT FROM AUTHOR]

• A new Crowdshipping (CS) initiative for Public Transportation (PT) systems is proposed. • The intention of PT passengers to participate in the initiative is explored. • Prohibiting factors in way of participation are identified using a qualitative analysis. • The sensitivity of passengers with different characteristics to the prohibiting factors are revealed. An emerging stream of Crowd-Shipping (CS) solutions focuses on existing momentum in Public Transportation (PT) to ship viable delivery packages by PT passengers. Few studies have explored the package delivery acceptance behavior of passengers engaged in PT-based CS initiatives while passengers' behavioral intention to participate (i.e., engage) is not studied. It is requisite that newly introduced CS platforms explore their potential crowdshippers' behavior on intention to participate and set efficient marketing strategies. Given survey data collected from 2208 PT passengers in Sydney metropolitan area, this study explores the intention of PT passengers as crowd-shippers to participate in PT-based CS initiatives, as well as prohibiting factors in way of participation. Accordingly, a binominal logit model is developed whereby the variables impacting the intention to participate are identified. Then, using an inductive thematic analysis, 917 reasons (text responses) for not participating are scrutinized, and the prohibiting factors are identified and categorized. Considering demographic and socio-economic characteristics of the respondents, the study reveals to what degree passengers with different characteristics are sensitive to prohibiting factors. This research provides several practical insights that can assist in successfully defining, launching, and advertising a new PT-based CS initiative. As a key finding, it is observed that women, full-time employees, elderly, retirees, and low-income PT passengers hardly participate, while the youth, individuals with a positive attitude towards sustainable freight initiatives, and those who experienced working with parcel lockers would participate with a higher probability. Moreover, it is observed that factors relating to time availability/flexibility and physical health condition/importance of passengers are much more important than the compensation level for passengers to accept to participate in PT-based CS initiatives. [ABSTRACT FROM AUTHOR]

We introduce a model of random ambiguity aversion. Choice is stochastic due to unobserved shocks to both information and ambiguity aversion. This is modeled as a random set of beliefs in the maxmin expected utility model of Gilboa and Schmeidler (1989). We characterize the model and show that the distribution of ambiguity aversion can be uniquely identified from binary choices. A novel stochastic order on random sets is introduced that characterizes greater uncertainty aversion under stochastic choice. If the set of priors is the Aumann expectation of the random set, then choices satisfy dynamic consistency. This corresponds to an agent who knows the distribution of signals but is uncertain about how to interpret signal realizations. More broadly, the analysis of stochastic properties of random ambiguity attitudes provides a theoretical foundation for the study of other random nonlinear utility models. [ABSTRACT FROM AUTHOR]

Pursuing the overarching goal of saving both lives and healthcare costs, we introduce an approach to increase the expected participation in a preventive healthcare program, e.g., breast cancer screening. In contrast to sick people who need urgent medical attention, the clients in preventive healthcare decide whether to go to a specific facility (if this maximizes their utility) or not to take part in the program. We consider clients' utility functions to include decision variables denoting the waiting time for an appointment and the quality of care. Both variables are defined as functions of a facility's utilization. We employ a segmentation approach to formulate a mixed-integer linear program. Applying GAMS/CPLEX, we optimally solved instances with up to 400 demand nodes and 15 candidate locations based on both artificial data as well as in the context of a case study based on empirical data within one hour. We found that using a Benders decomposition of our problem decreases computational effort by more than 50%. We observe a nonlinear relationship between participation and the number of established facilities. The sensitivity analysis of the utility weights provides evidence on the optimal participation given a specific application (data set, empirical findings). [ABSTRACT FROM AUTHOR]

The logit‐mixed logit (LML) model advances choice modeling by generalizing previous parametric and semi‐nonparametric specifications and allowing retrieval of flexible taste distributions. Using standard operating conditions in the field, we report results from Monte Carlo experiments designed to assess the finite sample bias‐variance tradeoff for the LML using as a benchmark conventional Mixed logit models (MXL) under asymmetric and multimodal taste distributions. The LML specification always outperforms the MXL in terms of bias, but when the variance around modes is high the mean squared error (MSE) is lower than that of MXL only at sample sizes larger than usual and with some nuances. D‐error minimizing experimental design predicated on multinomial logit significantly reduces MSE, but no clear winner is found between polynomial, step, and spline functions for the multidimensional grid function. Analysis of empirical data from a choice experiment on tap water shows that multimodality emerges only if higher number of node parameters are used in the LML. [ABSTRACT FROM AUTHOR]

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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.

• Demonstrate issues with existing Path Size Logit (PSL) models. • Propose a new internally consistent Adaptive PSL that addresses these issues. • Proof of existence and uniqueness conditions for Adaptive PSL solutions. • Adaptive PSL likelihood formulation and tracked route data MLE procedure. • Estimation of PSL models on a large-scale network using real GPS data. Path Size Logit route choice models attempt to capture the correlation between routes by including correction terms within the route utility functions. This provides a convenient closed-form solution for implementation in traffic network models. The path size terms measure distinctiveness of routes; a route is penalised based on the number of other routes sharing its links, and the costs of those shared links. Typically, real road networks have many very long routes that should be considered unrealistic. Such unrealistic routes are problematic for the Path Size Logit (PSL) model because they negatively impact the choice probabilities of realistic routes when links are shared. The Generalised Path Size Logit (GPSL) model attempts to address this problem by weighting the contributions of routes to path size terms according to the ratio of route travel costs. However, the GPSL model is not internally consistent in how it defines routes as being unrealistic: the path size terms consider only travel cost, whereas the route choice probability relation considers disutility including the correction term. To solve these challenges, this paper formulates a new internally consistent Adaptive Path Size Logit (APSL) model wherein routes contribute to path size terms according to the ratio of route choice probabilities, ensuring that routes defined as unrealistic by the path size terms, are exactly those with very low choice probabilities. The APSL route choice probability relation is an implicit function, naturally expressed as a fixed-point problem. A proof is provided for the guaranteed existence of solutions, as well as conditions for the uniqueness of solutions. A Maximum Likelihood Estimation procedure is given for estimating the APSL model with tracked route observation data, and this procedure is investigated in a simulation study where it is shown it is generally possible to reproduce assumed true parameters. APSL is then estimated using real tracked route GPS data on a large-scale network, and results are compared with other PSL models.. [ABSTRACT FROM AUTHOR]

There is a considerable gap between the existing knowledge used to estimate the value of time in passenger transport when compared with the framework of analysis developed for freight transport. In this paper, we applied a model that is gaining recognition in the context of passenger values of time estimation known as random valuation (RV) to a context in which there is little evidence of its application: freight value of time estimation (FVOT). Secondly, we compared this model to the commonly used methodology, random utility (RU). We analysed several levels of model specification to estimate the FVOT using data from a stated choice experiment applied in five of the main cargo transport road corridors of Colombia. The analysis accounts for the value of time differentiated by vehicle type. Results suggest that the RV models performed better for the same dataset than its counterpart RU in all cases. Another aspect to highlight is that the FVOT value obtained with the RU approach was pretty similar to those obtained with the RV in most cases. Also, the estimates FVOT for empty trips are closer, and in some cases higher, than those of loaded trips. [ABSTRACT FROM AUTHOR]

Models of choice over menus aim at capturing the effect of some behavioral or non-standard element of decision-making on the behavior of a single decision-maker. These models are usually compared with the standard model of choice over menus, in which the decision-maker chooses a menu whose best item is better than that of all other available ones. However, in many empirical settings such as experimental studies, choice data come from a population of decision-makers with possibly heterogeneous attitudes and tastes. This heterogeneity can make the observed choices over menus stochastic. This fact calls for a stochastic characterization of models of choice over menus to be able to better compare and contrast different models empirically. In this paper, I do this task for the standard model, which would be an extension of the random utility model to the realm of choice over menus. In particular, I provide the necessary and sufficient conditions, i.e., axioms on (stochastic) choice data over menus for it to be consistent with a population of decision-makers each of whom behaves according to the standard model. The axioms that characterize the model are the axiom of revealed stochastic preferences over singletons and three rationality axioms. [ABSTRACT FROM AUTHOR]

• Intertemporal preference elicited by matching tasks shows behavioral regularities. • Non-evidence-accumulation models are developed for such matching tasks. • These models outperform evidence-accumulation models in explaining the regularities. • Evidence accumulation is not essential for generating intertemporal preference. Intertemporal preference has been investigated mainly with a choice paradigm. However, a matching paradigm might be more informative for a proper inference about intertemporal preference and a deep understanding of the underlying cognitive mechanisms. This research involved two empirical studies using the matching paradigm and compared various corresponding dynamic models. These models were developed under either the framework of decision field theory, an exemplar theory assuming evidence accumulation, or a non-evidence-accumulation framework built upon the well-established notions of random utility and discrimination threshold (i.e., the RUDT framework). Most of these models were alternative-based whereas the others were attribute-based ones. Participants in Study 1 were required to fill in the amount of an immediate stimulus to make it as attractive as a delayed stimulus, whereas those in Study 2 needed to accomplish a more general matching task in which either the payoff amount or delay length of one stimulus was missing. Consistent behavioral regularities regarding both matching values and response times were revealed in these studies. The results of model comparison favored in general the RUDT framework as well as an attribute-based perspective on intertemporal preference. In addition, the predicted matching values and response times of the best RUDT model were also highly correlated with the observed data and replicated most observed behavioral regularities. Together, this research and previous modeling work on intertemporal choice suggest that evidence accumulation is not essential for generating intertemporal preference. Future research should examine the validity of the new framework in other preferential decisions for a more stringent test of the framework. [ABSTRACT FROM AUTHOR]

Consideration sets have become a central concept in the study of consumer behavior. Frequently, consumers are asked to split choice alternatives into those that that they would consider and those that they would not. Information on alternatives not in the consideration set is then typically not used in subsequent analysis. This practice is shown to lead to biased estimates of preference parameters. The reason for this is shown to be a form of sample selection bias.

A growing number of stochastic choice models include a "default" option for situations where the decision maker selects none of the feasible alternatives. While this is a welcome development, these models also present an empirical challenge—since it may be difficult to observe situations where the decision-maker chooses nothing. Taking Manzini and Mariotti's (2014) independent random consideration model as a point of departure, I investigate what can be learned about models of choice with default when the no-choice behavior of the decision-maker is unobservable. [ABSTRACT FROM AUTHOR]

We provide an axiomatic analysis of dynamic random utility, characterizing the stochastic choice behavior of agents who solve dynamic decision problems by maximizing some stochastic process (Ut) of utilities. We show first that even when (Ut) is arbitrary, dynamic random utility imposes new testable across‐period restrictions on behavior, over and above period‐by‐period analogs of the static random utility axioms. An important feature of dynamic random utility is that behavior may appear history‐dependent, because period‐t choices reveal information about Ut, which may be serially correlated; however, our key new axioms highlight that the model entails specific limits on the form of history dependence that can arise. Second, we show that imposing natural Bayesian rationality axioms restricts the form of randomness that (Ut) can display. By contrast, a specification of utility shocks that is widely used in empirical work violates these restrictions, leading to behavior that may display a negative option value and can produce biased parameter estimates. Finally, dynamic stochastic choice data allow us to characterize important special cases of random utility—in particular, learning and taste persistence—that on static domains are indistinguishable from the general model. [ABSTRACT FROM AUTHOR]

Increase in the level of mechanization in agriculture is a factor that promotes increase in productivity and also helps improve life standards in rural regions. In this study, factors that affect participation in benefiting from machinery and equipment supports were studied through the Ağrı province example. The main material of the study consisted of primary data that was obtained from face to face meetings carried out with agricultural enterprises, which were registered in the Farmer Registration System of Ağrı province. The Probit Regression Analysis method was used to determine participation in benefiting/not benefiting from the support, the relationships between the socioeconomic factors that affect this act, as well as the relationship degrees. According to the results obtained by the model, while the variables of household population, production of arable crops, and the existence of nonagricultural income had a significantly negative effect on participation in benefiting from machinery and equipment supports; the variables of irrigated land size, total amount of agricultural fields in the enterprise, and the existence of tractor(s) in the enterprise had a significantly positive effect. [ABSTRACT FROM AUTHOR]

The standard framework for modeling stochastic choice, the random utility model, is agnostic about the temporal dynamics of the decision process. In contrast, a general class of bounded accumulation models from psychology and neuroscience explicitly relate decision times to stochastic choice behavior. This article demonstrates that a random utility model can be derived from the general class of bounded accumulation models, and characterizes how the resulting distribution of random utility depends on response time. This relationship can bias the estimation of structural preference parameters. The bias can be alleviated via the inclusion of standard observables directly in the econometric specification, or through incorporating novel observables such as response time or neurobiological data. Examples of estimating risk and brand preferences are pursued. This paper was accepted by Matthew Shum, marketing. [ABSTRACT FROM AUTHOR]

This note generalizes Gul and Pesendorfer’s random expected utility theory, a stochastic reformulation of von Neumann-Morgenstern expected utility theory for lotteries over a finite set of prizes, to the circumstances with a continuum of prizes. Let [0, M] denote this continuum of prizes; assume that each utility function is continuous, let C0[0,M] be the set of all utility functions which vanish at the origin, and define a random utility function to be a finitely additive probability measure on C0[0,M] (associated with an appropriate algebra). It is shown here that a random choice rule is mixture continuous, monotone, linear, and extreme if, and only if, the random choice rule maximizes some regular random utility function. To obtain countable additivity of the random utility function, we further restrict our consideration to those utility functions that are continuously differentiable on [0, M] and vanish at zero. With this restriction, it is shown that a random choice rule is continuous, monotone, linear, and extreme if, and only if, it maximizes some regular, countably additive random utility function. This generalization enables us to make a discussion of risk aversion in the framework of random expected utility theory. [ABSTRACT FROM AUTHOR]

This paper investigates the environmental trade offs resulting from the adoption of autonomous vehicles (AVs) as a function of modal shifts and use phase. An empirical approach is taken to formulate a mode choice model informed from a stated preference (SP) survey conducted in Madison, Wisconsin. A life cycle analysis based on well-to-wheel model is then conducted to quantify the use phase environmental impacts across different categories. The mode choice analysis reveals the potential users of AVs and its attractiveness as a mode of transportation, ultimately competing with traditional modes available. This translates into modal shifts that are shown to result in an expected increase in environmental impacts across all studied categories: energy consumption (5.93%), greenhouse gas emissions (5.72%), particulate matter (6.80%), sulfur (6.85%) and nitrogen oxides (5.70%). The adoption of electric AVs (E-AVs) is then analyzed as an offsetting strategy to combat the increase in environmental impacts. The analysis reveals that E-AVs are capable of offsetting the foreseen impacts, yet their effectiveness is dependent on the electricity generation mix and adoption rate.

An extended multi-grid model is proposed to study fire evacuation in a two-exit room. The exit selection based on random utility theory, as well as the pedestrian movement in fire, is investigated. The effects of different occupant types, the utility threshold, heat release rate of fire, burning materials and pre-movement time on evacuation are discussed. The results show that active occupants are beneficial for evacuation because of their guidance to the herding pedestrians, whereas, the existence of conservative is not always good for evacuation; a proper frequency of changing target exit can relieve congestion and optimize evacuation process; evacuation time is not monotonically increasing with the increment of heat release rate due to acceleration when pedestrians feel the incentive of high temperature within limit; the effect of burning material on evacuation is related to its thermal physical properties; the pre-movement time aggravates the difficulty of evacuation due to the bad visibility and high CO concentration in fire situation. The study may be useful to predict exit selection and pedestrian movement process, and then give suggestions to guide pedestrian evacuation under fire emergency. [ABSTRACT FROM AUTHOR]

Traditionally, descriptive accounts of intertemporal choice have relied on static and deterministic models that assume alternative-wise processing of the options. Recent research, by contrast, has highlighted the dynamic and probabilistic nature of intertemporal choice and provided support for attribute-wise processing. Currently, dynamic models of intertemporal choice—which account for both the resulting choice and the time course over which the construction of a choice develops—rely exclusively on the framework of evidence accumulation. In this article, we develop and rigorously compare several candidate schemes for dynamic models of intertemporal choice. Specifically, we consider an existing dynamic modeling scheme based on decision field theory and develop two novel modeling schemes—one assuming lexicographic, noncompensatory processing, and the other built on the classical concepts of random utility in economics and discrimination thresholds in psychophysics. We show that all three modeling schemes can accommodate key behavioral regularities in intertemporal choice. When empirical choice and response time data were fit simultaneously, the models built on random utility and discrimination thresholds performed best. The results also indicated substantial individual differences in the dynamics underlying intertemporal choice. Finally, model recovery analyses demonstrated the benefits of including both choice and response time data for more accurate model selection on the individual level. The present work shows how the classical concept of random utility can be extended to incorporate response dynamics in intertemporal choice. Moreover, the results suggest that this approach offers a successful alternative to the dominating evidence accumulation approach when modeling the dynamics of decision making. [ABSTRACT FROM AUTHOR]

Stochastic User Equilibrium (SUE) models allow the representation of the perceptual and preferential differences that exist when drivers compare alternative routes through a transportation network. However, as an effect of the used choice models, conventional applications of SUE are based on the assumption that all available routes have a positive probability of being chosen, however unattractive. In this paper, a novel choice model, the Bounded Choice Model (BCM), is presented along with network conditions for a corresponding Bounded SUE. The model integrates an exogenously-defined bound on the random utility of the set of paths that are used at equilibrium, within a Random Utility Theory (RUT) framework. The model predicts which routes are used and unused (the choice sets are equilibrated), while still ensuring that the distribution of flows on used routes accords to a Discrete Choice Model. Importantly, conditions to guarantee existence and uniqueness of the Bounded SUE are shown. Also, a corresponding solution algorithm is proposed and numerical results are reported by applying this to the Sioux Falls network. [ABSTRACT FROM AUTHOR]

To establish an effective parking management policy, it is essential to understand how drivers make their decision on parking. This study models the parking choice behavior of drivers who make a shopping trip to Siam Square, Bangkok, Thailand. The parameters are estimated from the result of stated preference questionnaires in the form of utility function. Data were successfully collected from 228 decisions of 76 respondents. The study found that the drivers who park from 0 to 3 h make decisions based on similar factors and sensitivity. The most influential factors included the parking fee and walking distance. Meanwhile the long-term parking decisions were based on the aforementioned 2 attributes and also the search time. [ABSTRACT FROM AUTHOR]

We consider a bilevel optimization framework corresponding to a monopoly spatial pricing problem: the price for a set of given facilities maximizes the profit (upper level problem) taking into account that the demand is determined by consumers' cost minimization (lower level problem). In our model, both transportation costs and congestion costs are considered, and the lower level problem is solved via partial transport mass theory. The partial transport aspect of the problem comes from the fact that each consumer has the possibility to remain out of the market. We also generalize the model and our variational analysis to the stochastic case where utility involves a random term. [ABSTRACT FROM AUTHOR]