Rational Preference Research Articles

Incentive and Supervision Mechanism for Operation and Maintenance of Mega Railway Infrastructure Based on Principal-Agent Theory

This study incorporates fairness preference theory into the principal-agent model to address the challenges arising from information asymmetry between the government and railway infrastructure operation and maintenance (O&M) companies. It develops an incentive and supervision model for the management of mega railway infrastructure operations and maintenance, considering two perspectives: full rationality and fairness preference. The models are compared and analyzed, exploring the costs of government supervision and the changes in expected benefits when equity preference is taken into account. The conclusions are supported by numerical examples and visual analysis, illustrating the impact of different variable parameters on the behavioral strategies of the government and O&M companies. The findings demonstrate that a well-designed incentive and supervision mechanism can effectively regulate the behavior of railway infrastructure O&M companies. The effort level of O&M companies is positively correlated with the intensity of government incentives and supervision. Moreover, from the perspective of fairness preference, transferring some financial benefits from the government to O&M companies not only increases their extra effort but also reduces government’s supervision costs, while the government’s expected utility gain depends on the strength of “reciprocity” exhibited by O&M companies.

On preferences and reward policies over rankings

We study the rational preferences of agents participating in a mechanism whose outcome is a ranking (i.e., a weak order) among participants. We propose a set of self-interest axioms corresponding to different ways for participants to compare rankings. These axioms vary from minimal conditions that most participants can be expected to agree on, to more demanding requirements that apply to specific scenarios. Then, we analyze the theories that can be obtained by combining the previous axioms and characterize their mutual relationships, revealing a rich hierarchical structure. After this broad investigation on preferences over rankings, we consider the case where the mechanism can distribute a fixed monetary reward to the participants in a fair way (that is, depending only on the anonymized output ranking). We show that such mechanisms can induce specific classes of preferences by suitably choosing the assigned rewards, even in the absence of tie breaking.

Revealed Privacy Preferences: Are Privacy Choices Rational?

We investigate the extent to which trade-offs involving the sharing of personal information exhibit consistency with an underlying rational preference for privacy. In an experiment, people engage in trade-offs across two domains of personal information sharing, allowing us to classify whether their choices satisfy the generalized axiom of revealed preference. They also subsequently price the sharing of their personal information. Sixty-three percent of subjects act consistently with a rational preference ordering when allocating privacy levels, a level comparable to that observed in other domains. Individuals who are inconsistent when engaging in simple privacy trade-offs exhibit substantially more costly preference reversals when pricing personal information sharing. Only one-third of subjects exhibit consistency across both the selection of personal information to share and the pricing of such information sharing. Our results imply that monetizing personal information can have distinct welfare consequences for people with different degrees of rationality in their underlying ability to make sensible trade-offs involving personal information. We also provide evidence that preferences elicited over choices in our experiment correlate with real-world privacy behaviors. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This work was supported by Forschungskredit (University of Zurich) [Grant FK-17-017] and Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 174630] to Y.S. Lee. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00807 .

Literary Metaphor Comprehension in the Rational and Experiential Cognitive Styles of Human Thinking

Prior research on the poetic metaphor interpretation has focused on describing metaphoric structure and identifying its principles by observing semantically mapped properties of a metaphor. Cognitive literary studies have highlighted an experimental approach to the metaphoric thinking. The experimental approach examines, among others, the empirical processes of metaphor interpretation and comprehension. For metaphor processing, emergent meanings are crucial. We already have many reports, findings and normative data on metaphor processing, but individual cognitive mechanisms and variations across people have received an insufficient attention. To address this issue, this empirical study examines the influence of rational (analytical) and experiential (intuitive) cognitive styles on the comprehension of a poetic literary metaphor. In our statistical analysis, we have used a fractional pooling of the participants. It highlights the non-identical metaphor processes in rational and experiential cognitive preferences. According to our findings, people with a preference for the experiential cognitive style produce relatively more emergent metaphorical meanings than people with a preference for the rational cognitive style. These findings contribute to the more complex knowledge of the metaphoric thinking consequences. Keywords: Literary metaphor; Interpretation of metaphor; Rational (analytical) cognitive style; Experiential (intuitive) cognitive style; Emergent metaphorical meaning

Rational preference knowledge influence on the evolutionary equilibrium of carbon reduction negotiation: An exploration based on timed petri net

In the digital economy age, carbon emission reduction is perhaps the most crucial contribution affecting national activities and international negotiations. In a potential carbon reduction conflict, a rational participant expects to select a suitable strategic opinion to reach another state with improved benefit. Participants’ preferences reflect their priority ranking on multiple possible conflict solutions, directly determining the evolutionary direction and equilibrium solutions. Referring to the potential carbon reduction conflicts, Timed Petri Net for Conflict Analysis (TPNCA) is proposed as a graphical tool to explore participants’ rational preferences, state transformation, and conflict evolutionary equilibrium. The generation rule of TPNCA is designed according to carbon reduction co-operators’ rational preference knowledge. From the long-term perspective, dynamic unilateral improvement is proposed to reflect decision-makers’ strategic initiative willingness to reach end nodes. Dynamic equilibrium conditions are conducted to determine the final stability state for a conflict. This study reveals how participants’ preference knowledge and time constraints influence conflict evolutionary and dynamic equilibria. Climate group should positively engage in carbon reduction negotiations and flexibly adjust national tasks, according to real-time carbon reduction contributions. This study (1) reveals the influence of preference knowledge on conflict evolution, (2) explores the role of time constraints in determining a terminated node, (3) proposes dynamic equilibrium from the long-term perspective, and (4) helps guide participants’ strategic decision-making for mutually solving carbon reduction.

How to promote knowledge transfer within R&D team? An evolutionary game based on prospect theory

Knowledge transfer is the basis for R&D teams and enterprises to improve innovation performance, win market competition and seek sustainable development. In order to explore the path to promote knowledge transfer within the R&D team, this study considers the bounded rationality and risk preference of individuals, incorporates prospect theory into evolutionary game, constructs a perceived benefits matrix distinct from the traditional benefits matrix, and simulates the evolutionary game process. The results show that, R&D personnel’s knowledge transfer decisions depend on the net income difference among strategies; only if perceived cost is less than the sum of perceived synergy benefit, perceived organization reward value, and perceived organization punishment value, can knowledge be fully shared and transferred within the R&D team. Moreover, R&D personnel’s knowledge transfer decisions are interfered by the irrational psychological factors, including overconfidence, reflection, loss avoidance, and obsession with small probability events. The findings help R&D teams achieve breakthroughs in improving the efficiency of knowledge transfer, thereby enhancing the capacity of enterprises for collaborative innovation.

A Local Adjustment Method to Improve Multiplicative Consistency of Fuzzy Reciprocal Preference Relations

Preferences that verify the transitivity property are usually referred to as rational or consistent preferences. Existent methods to improve the consistency of inconsistent fuzzy reciprocal preference relations (FPRs) fail to retain the original preference values because they always derive a new FPR. This article presents a new inconsistency identification and modification (IIM) method to detect and rectify only the most inconsistent elements of an inconsistent FPR. As such, the proposed IIM can be considered a local adjustment method to improve multiplicative consistency (MC) of FPRs. The case of inconsistent FPRs with missing values, i.e., incomplete FPRs, is addressed with the estimation of the missing preferences with a constrained nonlinear optimization model by the application of the IIM method. The implementation process of the proposed algorithms is illustrated with numerical examples. Simulation experiments and comparisons with existent methods are also included to show that the new method requires fewer iterations than existent methods to improve the MC of FPRs and achieves better MC level, while preserving the original preference information as much as possible than the existent methods. Thus, the results presented in this article demonstrate the correctness, effectiveness, and robustness of the proposed method.

Research on Supply Chain Emission Reduction Decisions Considering Loss Aversion under the Influence of a Lag Effect

Considering that a lag effect exists in R&D investment, investigating the impacts of manufacturers’ resulting loss-averse behavior on R&D investment in carbon emission reduction technologies is important. This paper establishes three differential game models, namely centralized decision making, decentralized decision making with the manufacturers’ rational preferences, and decentralized decision making with the manufacturers’ loss-aversion preferences. The models are used to analyze the mechanism of the lag effect and loss aversion on manufacturers’ R&D investment in emission reduction, based on a two-level supply chain consisting of manufacturers and retailers. This study finds that: (1) The lag effect can encourage manufacturers to invest in the R&D of emission reduction technologies. (2) There is a threshold value for the lag time, and only when the lag time is higher than this threshold value will manufacturers display loss-averse behavior. (3) When manufacturers’ degree of loss aversion is small, loss-averse behavior has a negative effect on their investment in the R&D of emission reduction technologies, while the opposite has a positive effect.

The argument from small improvement is a red herring

ABSTRACT The much-discussed ‘Argument from Small Improvement’ has been advanced both as a reason to reject (tripartite) Completeness, one of the standard axioms of decision theory, and to accept the possibility of rationally incomparable choices. But this form of argument cannot be an effective basis for either of these conclusions, unless one already has some prior, independent reason to prefer Transitivity to Completeness as a constraint on rational preferences (or rational values). In particular, I show how a reverse argument from small improvement can be constructed, starting from the assumption of tripartite Completeness, to the conclusion that Transitivity fails. I conclude that this form of argument as it has been standardly presented in the literature is a kind of ‘red herring’. We can only make progress here by evaluating the reasons independent of either such argument to prefer one or other of Transitivity or Completeness.

Blockchain-Based Design of a Government Incentive Mechanism for Manufacturing Supply Chain Data Governance

In the context of “double carbon”, constructing green supply chains is the only way to implement sustainable development strategies in the manufacturing industry. This paper, therefore, examines the manufacturing supply chain for low-carbon products. More recently, the lack of technical information flow due to data barriers up and down the supply chain has led to high energy consumption, the serious waste of raw materials, and the substandard production of green products. Therefore, the level of supply chain data governance must be improved to enhance the sustainability of the supply chain. By studying blockchain-based data governance and government policy incentives for manufacturing supply chains, this study constructed an evolutionary game model based on prospect theory for the tripartite relation of government, manufacturers, and retailers. The difference between the perceived and actual value was introduced into a three-way evolutionary game model based on prospect theory to optimize the practical implications of the model. The model was then simulated using system dynamics. Through the simulation, it could be concluded that the ability of the three-way evolutionary game to reach the optimal stability point is only related to the sensitivity of the retailer’s perceived value. Additionally, the outcome of the three-way evolutionary game can be unstable, with changes in perceived value sensitivity. Finally, relevant policy recommendations are made. The innovation of this study is establishing a data governance platform that uses data governance to build green supply chains. Additionally, the government was added to the subjects of the game to explore the role of government policy in data governance and sustainable development. In addition, the evolutionary game model was incorporated with prospect theory and traditional expected utility theory, and the rational deficits and preferences of decision makers were taken into account, which brings the results closer to the reality of the situation.

Random utility and limited consideration

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)).

Quantification of Aversion to Uncertainty in Intertemporal Choice through Subjective Perception of Time

Intertemporal choices are those decisions structured over several periods in which the effects only manifest themselves with the passage of time. The main mathematical reference for studying the behavior of individuals with respect to this type of decision is the Discounted Utility Model which hypothesizes completely rational individuals. The empirical evidence that deviates from normative expectations has motivated the formulation of alternative models with the aim of better describing the behavior of individuals. The present paper investigates the characteristics behind hyperbolic discounting starting from the phenomenon of decision inconsistency, i.e., when individuals’ preferences vary over time. The mechanisms of inconsistency will be explored through the physical concept of relative time, proving the importance of uncertainty aversion in the hyperbolic trend of the discount function. The analysis of the mathematical characteristics of hyperbolic discounting and the relationship between decision inconsistency and subjective perception of time defines the maximum distance between rational and non-rational preferences. An experimental part empirically proves the relationship between uncertainty aversion and time inconsistency. The present paper contributes to the literature by defining a new characteristic of hyperbolic discounting and quantifying the impact of the subjective perception of time in the decision-making process.

Comparative study on consumers' choice behaviors in selecting pork in rational and irrational scenarios.

To better understand the purchasing decision-making process of humane pork, and examine the internal relationship between consumers’ preferences in rational consumption and irrational decoy scenarios, 405 consumers in Wuxi City, Jiangsu Province, and China were surveyed. Attributes were set for breeding time, breeding mode, diet cleanliness label, and price, and the first three among them reflect animal welfare conditions. The results show that in the rational consumption scenarios, consumers pay the most attention to the price attribute, followed by the attribute of diet cleanliness label, breeding mode, and breeding time. In the irrational decoy scenarios, consumers are most likely to be affected by the attribute decoy of diet cleanliness label, which have the utility of avoiding food safety risks. In addition, the decoy effect triggered by the price attribute which owned the highest degree of rational preference among consumers is also substantially higher, but lower than that of the diet cleanliness label attribute. The decoy effect caused by the breeding time attribute with the lowest degree of consumers’ rational preference is also the lowest. Therefore, the government should strengthen the publicity of the humane treatment of pigs before slaughter, and improve the certification and management system of pigs’ diet cleanliness. Besides, marketers are suggested to emphasis the product attributes that customer’s value the most in their advertising.

Developing an Optimal Peer-to-Peer Ride-Matching Problem Algorithm with Ride Transfers

Thanks to recent developments in ride-hailing transit services throughout the world, the peer-to-peer (P2P) ride-matching problem has been actively considered in academia in recent years. P2P ride-matching not only reduces travel costs for riders but also benefits drivers by saving them money in exchange for their additional travel time. However, assigning riders to drivers in an efficient way is a complex problem that requires focusing on maximizing the benefits for both riders and drivers. This study aims to formulate a multi-driver multi-rider (MDMR) P2P ride-matching problem, based on rational preferences and cost allocation for both driver and rider, which also allows riders to transfer between multiple drivers for their travels if needed. Tabu Search (TS) for system-optimal ride-matching and a Greedy Matching (GM) algorithm for stable ride-matching were developed to solve ride-matching cases. The results show that the developed algorithm can successfully solve the proposed P2P MDMR ride-matching problem. MDMR P2P ride-matching can be used in areas where not much demand for ride-sharing exists, or for long-distance travel. Also it can be applied to designs for a more efficient on-demand transit network which can allow for transfers between routes. Moreover, the comparison of results between two implemented approaches shows that system-optimal centralized ride-matching can bring more cost savings for all participants in the system, although it may not always be stable when riders and drivers can choose their ride-matching for their own maximum benefit.

A Boundedly Rational Decision-Making Model Based on Weakly Consistent Preference Relations

Completeness is one of the basic assumptions about the rational preference relation in classical decision theory. Strongly and weakly consistent preferences are presented by abandoning the completeness of the rational preference relation. Some expansion and contraction conditions are proposed and the relationships between these conditions of rationality are discussed. The relationships between the conditions of rationality and boundedly rational choice behavior based on strongly and weakly consistent preferences are analyzed and discussed. Furthermore, an example about the choices of chocolates with interval ordinal numbers is given to explain some of the main conclusions in this paper. The results can be used as references for the study of boundedly rational decisions.

The local bias in equity crowdfunding: Behavioral anomaly or rational preference?

AbstractWe use data on individual investment decisions to analyze whether investors in equity crowdfunding direct their investments to local firms and whether specific investor types can explain this behavior. We then examine whether investments exhibiting a local bias are more or less likely to fail. We show that investors exhibit a local bias, even when we control for those with personal ties to the entrepreneur. In particular, we find that angel‐like investors and investors with personal ties to the entrepreneur exhibit a larger local bias than regular crowd investors. Well‐diversified investors are less likely to suffer from this behavioral anomaly than investors with personal ties to the entrepreneur. Overall, we show that investors who direct their investments to local firms more often pick start‐ups that run into insolvency, which indicates that some local investments in equity crowdfunding constitute a behavioral anomaly rather than a rational preference. Moreover, our results reveal that platform design is an important factor determining the scope of the behavior anomaly.

Arrowian Social Equilibrium: Indecisiveness, Influence and Rational Social Choices under Majority Rule

Abstract We introduce the concept of an Arrowian social equilibrium that inverts the schemata of the famous impossibility theorem of Arrow (1950. “A Difficulty in the Concept of Social Welfare.” Journal of Political Economy 58 (4): 328–46) and captures the possibility of aggregating non-rational individual preferences into rational social preferences while respecting the Arrowian desiderata. Specifically, we consider individuals whose preferences may not be complete and who, accordingly, may be indecisive when faced with an issue. Breaking with tradition, we consider the possibility of such individuals drawing on their beliefs about society’s preferences that result from the aggregation process to resolve their indecisiveness. Formally, individual choices are modeled as a rational shortlist method (Manzini and Mariotti 2007. “Sequentially Rationalizable Choice.” The American Economic Review 97 (5): 1824–39), with own preferences followed by society’s as the pair of ordered rationales. This results in a mutual interaction between individual and social choices. We study this interaction using majority rule as the aggregator, with an Arrowian social equilibrium specifying how individual and social choices are co-determined, while requiring the latter to be rational. Our main result identifies minimal levels of societal indecisiveness needed to guarantee the existence of such equilibrium.

On the Estimation of Discrete Choice Models to Capture Irrational Customer Behaviors

The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economics has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.

Rational intransitive preferences

According to a widely held view, rationality demands that the preferences of a person be transitive. The transitivity assumption is an axiom in standard theories of rational choice. It is also prima facie very plausible. I argue here that transitivity is not a necessary condition of rationality; it is a constraint only in some cases. The argument presented here is based on the non-linearity of differential utility functions. This paper has four parts. First, I present an argument against the transitivity assumption (I, II). Second, I discuss some objections; this will also bring out some features of the view defended here, like the essentially comparative nature of choice (III). Then, I discuss certain proposals that promise to avoid my conclusion (IV). Finally, I go into some implications concerning the nature of rational decision making (V). To accept some intransitivities as rational does not make our life easier; it can even lead to hard choices. However, it does not lead to skepticism about rational choice.

Scientific and Methodological Approaches to the Assessment of National Research Universities

The strategy of innovative development of Russia requires the formation of national research universities of the international level. Twenty-nine Russian universities have already been awarded this status. They are faced with a difficult task - to become the movers of priority areas for the development of science, technology, economic sectors, and the social sphere. The paper discusses the issue of assessing the quality of the activities of universities in the category "National Research University" (NRU). The disadvantages of existing approaches to assessing the activities of universities are highlighted. It is proposed to use self-assessment with the involvement of independent experts as a tool for assessing the activities of the NRU and its improvement. A process model and criteria for assessing the quality of the activities of a national research university have been developed. A mathematical model of the preferences of a decision-maker (DM) for a comprehensive assessment of the quality of NRU activities has been built. The model takes into account the individual and rational preferences of decision makers, as well as the balance of approaches and results to ensure the quality of NRU activities. Estimates of the numerical values of the parameters of the developed mathematical model are determined. For this, an experiment was planned and a D-optimal supersaturated design was built. The quality indicators of the developed model are calculated, which allow us to conclude that the developed model better describes the results of the experiment than the existing models.