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We study agents' information acquisition decisions in a beauty contest game when they can access multiple information sources with different content and clarity. Each information source sends a signal with a common noise, and each agent observes this signal with an additional idiosyncratic noise. An information source has high content if it has low common noise and high clarity if it has low idiosyncratic noise. Theory predicts that under strong beauty contest incentives, agents ignore information from a source with high content if it has low clarity. Instead, they acquire equally costly information from a source with higher clarity despite its lower content. Our experimental results confirm the directional predictions; however the under-acquisition of the less-clear source is more severe than theoretical predictions. A source with low clarity is ignored even when its cost is negligible. Our findings provide a new strategic perspective to explain the differential impacts of content and clarity in corporate disclosures.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems.

We study the economic impact of perceptual limitations using experimental goods for which the difficulty of perceiving the difference between them can be manipulated by altering the similarity of their visual representation. In our first experiment, we found that subjects’ willingness-to-pay for goods became more similar when it was harder to discriminate between them. Building on this result, we ran a second experiment where the same experimental goods were traded in a market with heterogeneous buyer preferences and seller market power. Buyers were less likely to choose the option which maximises consumer surplus when discriminating between products was harder, and buyer payoffs were lower. We find indications that buyers used a different method of constructing their valuations in the market than in individual choice, and there was weak evidence that using different methods were beneficial for buyers. Seller prices and profits were not dependent on how easy it was for buyers to discriminate between goods.

Global value chains (GVCs) consist of “a large number of parts that interact in a non-simple way” (Simon, 1962: 468), and their governance is fraught with human behavioral challenges. Multinational enterprise (MNE) decision makers are cognizant of the complexity and challenges, and lead firms in GVCs attempt to address them with appropriate governance. Were that not the case, we would see more dark side outcomes of B2B relationships in GVCs. We use the New Internalization Theory (NIT) to explain and predict the governance choices made by lead MNEs and their GVC partners. Specifically, we take a micro-foundational perspective that builds upon the concepts of bounded rationality and bounded reliability. We point to three broad, non-mutually exclusive categories of governance tools – formal safeguards, relational tools, and entrepreneurship-oriented mechanisms – that can prevent, or at least mitigate, dark side outcomes. We illustrate with data from nine case studies of international GVCs.

How does the bounded rationality of managers affect pricing? We examine this under-researched question by examining how different psychological traits of managers relate to new product pricing practices and how these pricing practices, in turn, relate to new product performance. To do so, we survey 302 American marketing, sales, and pricing managers responsible for new product pricing decisions in business markets. Among others, our study identifies conformity and intuition as distinct psychological traits associated with pricing practices that have a positive relationship with new product performance. The main contribution of this study is the empirical demonstration that psychological traits offer valuable insights into how managers determine prices for new products in business markets.

Many applications of bilevel optimization contain a leader facing a follower whose reaction deviates from the one expected by the leader due to some kind of bounded rationality. We consider bilinear bilevel problems with follower's response uncertainty due to limited observability regarding the leader's decision and exploit robust optimization to model the decision making of the follower. We show that the robust counterpart of the lower level allows to tackle the problem via the lower level's KKT conditions.

Longstanding assumptions underlying strategic alliances, such as agency theory, are actively being revoked by dynamics in the new economy. The mechanism of inter-firm cooperation is increasingly being altered by radical developments in blockchains and artificial intelligence among other technologies. To capture and address this shift, this review takes a problematisation approach and focuses wholly on the pertinence of agency theory. First, it begins by acknowledging the established corpus in the area before, second, appraising the seven long-held assumptions in the principal-agent relationship encompassing (1) self-interest, (2) conflicting goals, (3) bounded rationality, (4) information asymmetry, (5) pre-eminence of efficiency, (6) risk aversion and (7) information as a commodity. Third, to add a fresh perspective, the review proceeds to proffer seven assumptions to advance a novel ‘Blockchain Agency Theory’ that would better describe new attributes and relaxed agency behaviour in blockchain alliances. These counter assumptions are (1) common interests, (2) congruent goals, (3) unbounded rationality, (4) information symmetry, (5) smart contracts, (6) mean risk and (7) information availability. In the fourth part, the prior audience of principals and agents is appraised and this culminates into, fifth, a consideration of a new audience of blockchain agency in algocratic environments. Altogether, the seven new assumptions extend and provoke new agency thinking among scholars and blockchain practitioners alike.

Boston Mechanism (BM) and Deferred Acceptance (DA) are two competing algorithms in school choice. BM is manipulable whereas DA encourages truth-telling. Recognizing heterogeneous sophistication of students, we use the level-k model to analyze students' strategies under BM in a complete information environment with strict school priorities. Because students tend to manipulate first choice under BM, we characterize the first choice reported by students at all possible sophistication levels. In a general framework we show that BM is no less efficient than DA, and in two specific belief settings we further show that it is as if students run DA in their minds when they make level-k reasoning under BM. We compare the relation between sophistication and welfare under BM in the two belief settings to emphasize the role of high sophistication and correct belief. We also compare our results with Nash equilibrium analysis.

The financial market provides a mechanism for aggregating information of heterogeneous traders, who have different beliefs, knowledge and trading strategies. This paper studies the interactions between heterogeneous traders and their impacts on price discovery by developing a pricing model for the futures market. With mathematical analysis, we solve the equilibrium and its stability conditions for the system. As the findings show, behavioral factors such as risk appetites, degree of rationality and market liquidity have a combined effect on stability conditions. When the stability conditions are satisfied, the market can aggregate the information to form “good knowledge” about the price. If investors have high risk appetites or a high degree of rationality, it is difficult for the market to realize the price discovery function.

In our previous study—area-partition-involved collaborative patrolling (APCP) game, we proposed a cooperative patrolling game by utilizing multiple patrol vehicles to protect the chemical cluster. Except for fixed countermeasures like intrusion detection, the APCP game was proved to reduce the security risks of chemical clusters being attacked by terrorists effectively. However, some general assumptions (i.e., complete information game and perfect rational players) in the game model make it difficult to be applied in reality since the game model is not robust enough to deal with some common uncertainties on attackers. In this paper, we extend the APCP game model by considering the uncertainties of the game model from two aspects: (1) One is the bounded rationality of attackers. Attackers may adopt the e-optimal response strategy without maximizing their payoffs strictly. In this circumstance, we propose the bounded rational APCP game solver. (2) The other is the uncertainties of the defender’s estimation on the attacker’s parameters. Some parameters regarding attackers are changed from exact values to distribution-free intervals. Correspondingly, we propose the interval APCP game solver. Through extensive simulations on a chemical cluster (i.e., the Antwerp Chemical Cluster), results of the case study indicate that when considering two types of uncertainties mentioned above, the defender’s payoff will be slightly reduced, whereas the more robust patrolling routes are more easily convinced and adopted by the management department of chemical clusters and improve the process safety of chemical clusters.

Individual decision-making, based on means-ends causality perceptions and allowing anticipation of how likely choices are to achieve goals, is defined as rational. Consequentialist rationality, which respects methodological individualism, allows us to distinguish unbounded rationality based on well-behaved preferences and no cognitive restrictions, and empirically guided bounded rationality theory which respects human cognition and psychology. In view of this methodological dualism, we mainly focus on the dynamics of boundedly rational reasoning, according to which one first specifies means and ends and then, based on more or less empirical evidence, tries to perceive how means determine the achievement of ends. To identify the dynamics of decision-making, we suggest observing (e.g., experimentally) not only the final decisions, but also their underlying deliberations. Such richer experimental data can substitute hitherto missing modules in the theory of bounded rationality: one directly asks participants what these modules are supposed to predict.

Cross-efficiency evaluation methods have long been suggested as an alternative for the ranking of decision making units (DMUs) in data envelopment analysis (DEA). So far, little research on cross-efficiency evaluation takes the bounded rationality of DMUs into account. In this paper, we propose a neutral cross-efficiency evaluation method based on interval reference points (IRPs) to consider bounded rational behavior. As such, we take the prospect value, which reflects the bounded rationality of DMUs when facing gain and loss, as secondary goals. This approach allows us to determine the input and output weights for each DMU from its own point of view. Each reference point (RP) of the prospect value is defined as an elastic interval reference point (IRP), which can degenerate into a precise reference point (PRP) or an IRP by adjusting the parameters. As a result, the proposed cross-efficiency evaluation method not only takes the bounded rational behavior of DMUs into account, but is also more neutral and flexible. Numerical examples are provided to illustrate the applications of the cross-efficiency evaluation method based on IRPs in DEA ranking.

This paper examines games with non-neutral option labels (such as “A”, “B”, “A”, “A”) and finds surprisingly invariant behaviour across games. The behaviour closely resembles the choices people make when they have to bet on one of the options in individual lotteries. An option’s ‘representativeness’ (lack of distinguishing features) and ‘reachability’ (physical centrality, salience, and valence) determine choice behaviour in both the lotteries and the highly strategic games. There is no evidence of people best-responding to others’ betting(-like) behaviour. This is in line with the idea that once people decide that strategic reasoning would not take them any further, they pick an alternative as if they were betting on one of their ‘current best-responses’. The findings explain the well-documented seeker advantage in hide-and-seek games, as well as why participants often display behaviour that could be exploited by others. On top, they help understand why in national lotteries, people also tend to bet on identical subsets of the available numbers.

Travelers always exhibit bounded rationality due to limited perception. Based on the theory of bounded rationality, switching thresholds under different patterns of information are analyzed in this paper. An experiment platform including four scenarios, such as historical descriptive information, real-time descriptive information, historical travel time information and real-time travel time information is designed to obtain the participants’ travel preference and route choice behavior. The relationship among route switching behavior, perceived travel time difference and travelers’ preference is analyzed based on experiment’s results. The Probit regression method is utilized for calibrating the switching threshold under different patterns of information. The results prove the dissimilarity of switching thresholds in diverse patterns of information. The switching threshold under real-time information is lower than that under historical information. The estimated results of risk parameters prove that the traveler has a slight tendency to pursue risk. The method proposed in this paper can effectively quantify the degree of bounded rationality.

The article discusses whether and to what extent experiments can contribute to a research paradigm based on the study of human behaviour in complex evolving environments and on the problem of asymmetric adjustment among different components of economic system along certain trajectories, focusing on the possibility that experimental evidence may represent an external consistency check on this type of heterodox modelling. It considers the evidence on rationality of human agents, and the possibility to identify a microfoundation alternative to homo oeconomicus, discussing the evidence on humans as strong reciprocators, as trusting individuals and as embedded in social norms.

To describe the dynamical ordering behaviour of a risk-averse newsvendor with trade credit in supply chain, two models are formulated for two scenarios based on the bounded rationality rule. Assuming the newsvendor chooses the ordering quantity by adaptively adjusting the choice of previous period, we first develop a deterministic model. Then, mathematical analysis of the model is provided, such as the stability of equilibrium points, Neimark–Sacker bifurcation and normal form. Besides, the diagrams such as bifurcation to chaos, Lyapunov exponent, attractors and time series are illustrated numerically. It is then followed by a stochastic model to reflect the unexpected random noises from external operational environment and the perturbation on the newsvendor behaviour. The sensitivity of equilibria, the confidence ellipse and the confidence band are investigated by utilising Stochastic Sensitivity Function technique. Analysis on stochastic sensitivity shows that the retailer’s dynamical ordering exhibits more complex behaviour than the deterministic counterpart.

This study extends the hybrid version of the baseline New-Keynesian model with heterogeneous agents who may adopt various forecast heuristics. With a focus on consumer expectations, we identify the most appropriate pairs of forecast heuristics that can lead to an equivalent fit to the data compared with the model specification under rational expectations. The competing specifications are estimated using the simulated method of moments. Our empirical results suggest that expectations under bounded rationality in the United States are grounded on consumers’ emotional state, while for the Euro Area they are technical in nature. This observation questions the need for a hybrid model specification under rational expectations.

Deep learning paradigm allowed computer scientists to take a fresh look at the format of knowledge representation and assimilation. Studies of artificial analogs of neurons and synaptic connections of the brain have indicated many significant regularities in the opposition of cognitive "medium" and cognitive information. This understanding gave a new impetus to the previously developed concept of embodied cognition in various branches of artificial intelligence. "Embodiment" is usually understood as a combination of cognitive and substrate components. At the same time, there remain world-systemic connections that involve a broader context in the dynamics of correlation between the subject of cognition (cognitive agent, bounded rationality) and the environment. The concept of embodied cognition assumes a clash of the range of cognitive systems, built upon different infogenesis and infotectonics (for example, different computing platforms and degrees of agency). The cross-modal Turing test is supposed to be a universal communication interface that allows “message” and “medium” of embodied cognitive agent to test each other. The use of reciprocal, environments and systems will allow a sequential cross-modal Turing test for two competing modules. Such an approach may turn out to be decisive in cyber-physical systems, which are born at the junction of diverse technical-scientific engineering solutions, as well as in systems that require a high learning rate and model correction. In neural network practice, this approach can be effective in the field of transfer learning, in which a pre-trained fragment of the network can be correlated with fundamentally irrelevant (for a neural network) data.

This paper studies aggregate equilibrium models where firms cannot compute future prices with perfect accuracy. Instead, firms use approximations to infer prices. In equilibrium, we find that the precision with which firms can compute prices is endogenous and depends on the level of aggregate supply. At the same time, firms’ individual supplies, and thus aggregate supply, depend on the precision with which future prices are computed. This interplay between supply and firms’ individual ability to infer prices induces multiple equilibria, with inefficiently low output, in economies that otherwise have a unique, efficient, rational expectations equilibrium. Moreover, exogenous parameter changes, which would increase output were there no computational frictions, can diminish the precision of agents’ price forecasts, and reduce output. Our model also accommodates the intuition that large interventions, such as unprecedented quantitative easing, can put agents into “uncharted territory.”

This paper models sovereign debt negotiations between a debtor country (player 1) and its international official-sector (player 2) and private-sector (player 3) creditors. The presence of a third player allows for the formation of coalitions between two players. If there are key players that are part of all credible coalitions, such as creditors jointly imposing a partial refinancing agreement on a small debtor country, restructuring negotiations can be impaired and may result in partial default resolutions. These suboptimal bargaining outcomes are triggered when creditors stop extending a full refinancing agreement upon experiencing regret when learning that a partial agreement would have offered a higher return in the past. The introduction of regret allows the model to capture the extend-and-pretend approach that creditors frequently adopt in sovereign debt negotiations. Bounded rationality in the form of concerns for past outcomes, however, is insufficient to create bargaining inefficiencies. Suboptimal refinancing agreements are instead due to the existence of bargaining strength asymmetries, which implies that limiting the dominating presence of key players may help to restore efficiency to sovereign debt negotiations.

International audience We use the analogy-based expectation equilibrium (Jehiel, 2005) to study cheap talk from a sender who does not perfectly understand all the messages available to him. The sender is endowed with a privately known language competence corresponding to the set of messages that he understands. For the messages that he does not understand, the sender has correct but only coarse expectations about the equilibrium response of the receiver. An analogy-based expectation equilibrium is always a Bayesian solution but usually differs from a standard communication equilibrium and from an equilibrium with language barriers (Blume and Board, 2013). We characterize conditions under which an outcome remains an equilibrium outcome when the sender's competence decreases. Partial language competence rationalizes information transmission and lies in pure persuasion problems, and can facilitate information transmission from a moderately biased sender.

In the power market, each entity is not completely rational when generate strategy, and the market information held by each entity is not exactly the same. In this paper, duopoly power providers with different selling adjustment structures are simplified from the actual grid background, where one provider can sell part of its power to another at contract price to store the power. Each provider is trying to maximize its profit by adjusting its power selling strategy. The process of evolutionary game with multi-periods bounded rational is established. One provider adjusts its selling strategy through the multi-periods market price and another through its multi-periods marginal profit. The quantity of power sold by each provider will tend to Nash equilibrium and how information asymmetry affects the stability of Nash equilibrium is analyzed through comparing dynamic power selling with and without information asymmetry. Information asymmetry has a great impact on one provider but not another. The numerical simulations also show that the information asymmetry will increase the stability region of the system. Different adjustment suggestions are proposed for different providers when information asymmetry occurs.

Bounded rationality of decision-makers and interaction among criteria plays a key role in the actual multi-criteria group decision-making (MCGDM) process. This paper aims to offer a decision approach to a MCGDM problem under hesitant fuzzy linguistic (HFL) uncertainty, by means of behavioral multigranulation decision-theoretic rough set (DTRS), where criteria interaction is handled by Choquet integral, and decision-makers’ psychological preferences are considered by prospect theory. In specific, we firstly define the behavioral DTRS over two universes, where the threshold parameters are obtained by maximizing expected utility, rather than minimizing expected risk. Then, from the perspective of the difference of alternatives, we develop the calculation methods of loss and benefit functions, and provide the determination of the overall threshold parameters by Choquet integral, in consideration of criteria interaction. With these discussions, the behavioral multigranulation DTRS over two universes in the HFL context is proposed, and the two special cases, i.e., optimistic and pessimistic versions are also discussed. Thus, solutions to a MCGDM issue, green supplier selection, by utilizing the proposed behavioral multigranulation HFL DTRS over two universes, are designed, along with key steps and algorithm. Ultimately, an illustrative example is given to elaborate the practicability and feasibility of our presented model.

Recent research has proposed a statistical test based on the notion that agents have bounded rationality, if and only if more attractive states are chosen with larger probability. We propose and implement a statistical test for bounded rationality in the context of stochastic cost frontiers. Bounded rationality is related to probabilistically cost-efficient distributions. The test is based on comparing a discrete set of probabilities with the theoretical distribution under bounded rationality. Implementation is shown to be quite easy in a Bayesian framework using the Bayes factor for model comparison between estimated and theoretical probabilities. The bounded-rationality model introduces only an extra parameter in frontier models and, therefore, it is quite practical to use in applications as a general semi-parametric model for inefficiency.

This paper presents a doubly dynamic day-to-day (DTD) traffic assignment model with simultaneous route-and-departure-time (SRDT) choices while incorporating incomplete and imperfect information as well as bounded rationality. Two SRDT choice models are proposed to incorporate imperfect travel information: One based on multinomial Logit (MNL) model and the other on sequential, mixed multinomial/nested Logit model. These two variants, serving as based models, are further extended with two features: bounded rationality (BR) and information sharing. BR is considered by incorporating the indifference band into the random utility component of the MNL model, forming a BR-based DTD stochastic model. A macroscopic model of travel information sharing is integrated into the DTD dynamics to account for the impact of incomplete information on travelers' SRDT choices. These DTD choice models are combined with within-day dynamics following the Lighthill-Whitham-Richards (LWR) fluid dynamic network loading model. Simulations on large-scale networks (Anaheim) illustrate the interactions between users' adaptive decision making and network conditions (including local disruption) with different levels of information availability and user behavior. Our findings highlight the need for modeling network transient and disequilibriated states, which are often overlooked in equilibrium-constrained network design and optimization. The MATLAB package and computational examples are available at https://github.com/DrKeHan/DTD, Comment: 40 pages, 19 figures

This paper considers a clearing service system where customers arrive according to a Poisson process, and decide to join the system or to balk in a boundedly rational manner. It assumes that all customers in the system are served at once when the server is available and times between consecutive services are independently and identically distributed random variables. Using logistic quantal-response functions to model bounded rationality, it first characterizes customer utility and system revenue for fixed price and degree of rationality, then solves the pricing problem of a revenue-maximizing system administrator. The analysis of the resulting expressions as functions of the degree of rationality yields several insights including: (i) for an individual customer, it is best to be perfectly rational if the price is fixed; however, when customers have the same degree of rationality and the administrator prices the service accordingly, a finite nonzero degree of rationality uniquely maximizes customer utility, (ii) system revenue grows arbitrarily large as customers tend to being irrational, (iii) social welfare is maximized when customers are perfectly rational, (iv) in all cases, at least 78% of social welfare goes to the administrator. The paper also considers a model where customers are heterogeneous with respect to their degree of rationality, explores the effect of changes in distributional parameters of the degree of rationality for fixed service price, provides a characterization for the revenue-maximizing price, and discusses the analytical difficulties arising from heterogeneity in the degree of bounded rationality.

I propose an Affect-Cognitive Theory to comprehensively understand how decisions occur in organizations. To this aim, I first review the assumptions of sensemaking and decision-making streams of research, especially the influence of bounded rationality, affective states and their relationships with cognition; then, I integrate them on the common basis of socially situated cognition. This new theory emphasizes the role of affective states in determining/being determined by cognition and its errors, pointing out decision makers’ affect as the result of multi-level adaptations to the physical and social environment. Management decisions are path dependent but not immutable; they, indeed, bank on the predominant feeling resulting from the modifying interactions and regulations of decision makers with their physical and social environment. Here, decision makers are proposed as “emotional cognizers” overcoming the thinking-feeling dichotomy that has often featured in the study of management decisions. This theory is beneficial for behavioral strategy, offering the needed assumptions to intertwine human cognition, emotions, and social behavior.

In this paper we investigate the risk-related effects of monetary policy in normal times, as well as in periods where the zero lower bound (ZLB) binds, in a stylized macroeconomic model with boundedly rational beliefs. In our model, financial market participants use heuristics to assess the risk premium over the policy rate in accordance to an “implicit Taylor rule” that measures the stance of conventional monetary policy and which serves as an informative instrument during times when the funds rate is constrained by the ZLB. In such a case, conventional monetary policy is exhausted so that the central bank is forced to use unconventional types of policy. We propose alternative monetary policy measures to help the economy out of the liquidity trap which take into account this assumed form of bounded rationality.

Recently, a new line of interdisciplinary research in economics, coined complexity economics, has emerged premised on the assumption that the economy should be treated as a complex system. Complexity economists borrow concepts and theories from evolutionary biology such as emergent-phenomena, positive-feedback, self-organization, and diversity. The stated goal of this new subfield is to create more realistic and detailed models of the economy. One popular approach to modeling involves the use of “fitness landscapes” to generate solutions spaces in the search for optima and to model adaptive behavior. Here, in place of production or utility functions, a fitness function provides a simplified, Darwinian evaluation of performance. But recently, there has been a turn toward self-reflection and criticality in the literature; specifically, a debate has surfaced over the limitations of the fitness landscape approach. Par example is the 2014 article titled “Economic Opportunity and Evolution: Beyond Landscapes and Bounded Rationality” appearing in Strategic Entrepreneurship Journal. The authors contest that existing fitness landscapes neglect the emergence of novelty – implicating an ever-changing solution space. However, novelty is not the only limitation of modeling economic phenomena using the fitness landscape. In this paper, I present a methodological assessment of the extant literature in an effort to redraw our attention to the limitations of the fitness landscape in achieving the goal of more realistic and detailed models. Are fitness landscapes serving this goal? If no, what are the limitations? And most importantly, how do we face the challenges head on?

Manzini and Mariotti (2014) define the menu-independent random consideration set rule, where the decision maker considers each alternative with a menu-independent probability known as the attention function. We relax the assumption of menu-independence and allow for any restriction to be imposed on the attention function. We show that there is an equivalence between the attention function and the hazard rate. This equivalence is used to characterize the menu dependent random consideration set rules that correspond to (i) specific conditions on the probability rule, and (ii) different stochastic choice models from the literature.

Centipede games represent a classic example of a strategic situation, where the equilibrium prediction is at odds with human behavior. This study is explicitly designed to discriminate among the proposed explanations for initial responses in centipede games. Using many different centipede games, our approach determines endogenously whether one or more explanations are empirically relevant. We find that non-equilibrium behavior is too heterogeneous to be explained by a single model. However, most non-equilibrium choices can be fully explained by level-k thinking and quantal response equilibrium but each model for different subjects. Preference-based models play a negligible role in explaining non-equilibrium play.

We identify a behavioral bias in some games with completely mixed equilibria. Following (Alcocer and Jeitschko, 2014) we characterize players who, when indifferent between several optimal choices, assign an equal probability to playing any one of them. We design an experiment to test for the presence of such ‘naive’ players. Our model fits the data better than the Nash equilibrium, based on simpler behavioral assumptions than those that rely on random payoff differences. In a first session, we sort subjects into naive players and their sophisticated counterparts. Two weeks later, each group played against varying proportions of automated players (bots) that follow varying off-equilibrium mixed strategies. Subjects categorized as naive continue to tend towards uniform mixing and also are less apt to account for distortions due to off-equilibrium bots. In contrast, sophisticated players do compensate for the distortions in the game, although this compensation is not large enough to restore equilibria, implying they are not ‘exploitation-proof.’ We also isolate altruistic components of players’ strategies: behavior skews further from Nash when doing so increases the total surplus subjects collectively obtain. Lastly, we show that the probability of being categorized as naive is correlated with the performance on a cognitive test.

People often value an item more when they own it than when it is available for purchase, and consequently are relatively reluctant to trade. This is the “endowment effect”, which has been widely documented in human populations and also in some non-human species. This paper develops a simple model in which it is adaptive to have a bias against trade, potentially explaining the basis of the endowment effect. The bias against trade arises from the strategic nature of trade in a moderately competitive environment: the interest of a potential trading partner in making the exchange is evidence that the decision maker already has the more valuable object. The model predicts that an endowment effect is promoted by large uncertainty about the fitness value of items, and also by conditions in which there are on average small gains to be had from trade. Because the model employs a simple bounded rationality heuristic for trade, it explains how the endowment effect could arise in species that lack theory of mind and related strategic reasoning abilities. The model also suggests an explanation for why endowment effects are so rarely observed in biological markets that exist between species. Because the trading classes have very different fitness functions, there is negligible competition across those classes. Consequently, there are substantial mutual gains to trade, so our model predicts there is unlikely to be adaptive pressure for an endowment effect.

In our real life, the decision maker is always of bounded rationality under risk and uncertainty. The decision result also can be impacted by the risk appetite of the decision maker. However, the existing research works of three-way decisions rarely involve the risk appetite of the decision maker. To address this drawback and enrich the application, we introduce the risk appetite into three-way decisions. More specifically, under the dual hesitant fuzzy environment, we utilize TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) as a valuable tool to handle the risk appetite character to construct risk appetite dual hesitant fuzzy three-way decisions. This study takes into account the risk appetite of decision maker and designs a series of decision analysis methodologies for three-way decisions. It can preferably satisfy the practical situation and vastly extend the range of applications. Firstly, based on the dual hesitant fuzzy loss functions, we mainly study dual hesitant fuzzy entropy and cross-entropy measures for determining the conditional probability. In this scenario, the conditional probability information can be objectively learned from the loss function matrix with the aid of the power average (PA) aggregated operator. Then, considering the different comparison methods of dual hesitant fuzzy elements (DHFEs), we further design two types of strategies to deduce risk appetite dual hesitant fuzzy three-way decisions in the framework of TODIM, i.e., Strategies 1 and 2, which mainly rely on the pairwise comparison of loss functions with DHFEs. Strategy 1 is designed based on the score function of DHFEs. Strategy 2 is the likelihood of DHFEs. Meanwhile, some properties are carefully investigated. Finally, the project investment evaluation of online peer-to-peer (P2P) is applied to illustrate and validate these proposed strategies.

In this paper, we address a design problem of nudging mechanism for dynamical decision makers with bounded rationality. We first present a mathematical model for dynamic decision makers with psychological biases. We then analyze the dynamics based on so-called δ-passivity, and reveal that the bias violates passivity of decision makers. We moreover propose a nudging mechanism based on the passivity paradigm to achieve desired social behavior. The presented mechanism is finally demonstrated through numerical simulation.

The indefinitely repeated prisoner's dilemma (IRPD) captures the trade-off between the short-term payoff from exploiting economic partners and the long-term gain from building successful relationships. We aim to understand more about how people form and use beliefs about others in the IRPD. To do so, we elicit beliefs about the supergame strategies chosen by others. We find that initial beliefs match behavior quite well and that most subjects choose strategies that perform well given their beliefs. Motivated by belief clustering, we use beliefs to estimate a level-k model of boundedly rational thinking. We analyze how beliefs and behavior evolve with experience: beliefs become more accurate over time, and we use beliefs to provide new evidence about the mechanism that underlies learning from experience. Finally, we find that a survey measure of trust predicts cooperative behavior and optimism about others' cooperation, which helps to explain how trust underpins successful economic exchanges.

With the development of intelligent decision-making, one kind of decision modes involves a large number of decision-makers (DMs), which is called large scale group decision making (LSGDM). In LSGDM, overconfidence is one of the common behaviors because of many DMs’ participation and the bounded rationality of human decision. Overconfidence usually has a negative impact on LSGDM and can even lead to failure in the final decision(s). To achieve consensus is very important for LSGDM. Different consensus models of LSGDM have been proposed, while the DMs’ overconfidence behaviors in the consensus have not been concerned. Hence, the purpose of this paper is to propose a consensus model which considers overconfidence behaviors, and the paper mainly focuses on LSGDM based on fuzzy preference relations with self-confidence (FPRs-SC). In the proposed model, a DM clustering method, which combines fuzzy preference values similarity and self-confidence similarity, is used to classify the DMs with similar opinions into a subgroup. A group consensus index which considers both the fuzzy preference values and self-confidence is presented to measure the consensus level among DMs. An overconfidence measurement is given to detect the DMs’ overconfidence behaviors in the consensus. Subsequently, the detailed overconfidence behavior analysis is presented involving two aspects: fuzzy preference values consensus and self-confidence consensus. A dynamic weight punishment mechanism is implemented for overconfident DMs to improve the consensus efficiently. The effectiveness and advantages of the presented consensus model are validated by a numerical example and comparative analysis.

The paper is devoted to a control science view of attitude change. We can think about it as an ”attitude control”. Here we are interested not in attitude change modeling like ”what will be change of the attitude if we infuence on some parameters of the human psychology”. We are interested in control question like ”what can be a control situation in case of control subject knows the human’s attitude change model” and the human knows about subject’s intention to control his attitude. In other words, let psychologists give us model of attitude change, and we concentrate on control problems about attitude: control subject’s goal and possibilities to control, the human’s goal and possible actions, mutual awareness and so on. We state control structure similar to one from organizational control models and append to it strategic thinking model as bounded rationality pattern.

The rapid growth of transportation data offers new opportunities to analyze the interaction between travel behavior and transportation system performance, and particularly when issues such as uncertainty and reliability are considered. Many previous studies in this area described the stationary behavior of stochastic transportation systems using user equilibrium (UE) conditions. In contrast, this paper develops a generalized Bayesian model to analyze the dynamic behavior of stochastic transportation systems. In the proposed model, the variability of link volume and travel time stems from the stochasticity in travel demand, transportation supply (e.g. link capacity, free flow travel time, etc.) and route choice. To the best of our knowledge, this is among the first work that considers the three sources of stochasticity simultaneously. In addition, we propose a Bayesian updating approach based on the Dirichlet model to describe the route choice behavior. This approach allows researchers to consider a wide range of route choice behavior of bounded rationality in day-to-day traffic dynamics, including a knowledge updating mechanism based on different memory lengths and weighting factors. This framework is particularly suitable for data-driven studies supported by emerging transportation data due to the computing efficiency of the Dirichlet-based Bayesian updating mechanism and the sound behavioral foundation. This paper shows that the proposed Bayesian model with infinite memory leads to UE conditions under stochastic demand and supply. Subsequently, a numerical case study is conducted to illustrate different day-to-day route choice dynamics with different memory lengths of system performance. This paper also discusses the influence of the three sources of stochasticity towards the aggregated variance of link volumes and travel time. With enough longitudinal travel choice and transportation system performance data, the proposed Bayesian framework could be empirically calibrated and tested, which offers an attractive descriptive alternative to the conventional UE-based transportation system models.

In this study, we develop an agent-system contingency theory as a general multi-level theory of managerial decisions in the supply chain and industrial marketing (SCIM) context. The proposed theory rests on two basic assumptions: agent decision authority and bounded rationality, and postulates that agent-level properties (i.e., relevant personality traits of human agents in SCIM) and system-level properties (i.e., inter-firm and/or intra-firm properties) can concurrently and interactively influence managerial decisions and actions in SCIM. We then propose the synergistic use of experiment and survey as a methodological framework to facilitate the empirical efforts in behavioral SCIM research oriented by the agent-system contingency theory. We also provide empirical illustrations regarding how the agent-system contingency theory and the accompanied methodological framework can be applied to behavioral SCIM research inquiries, using two studies in the decision contexts of opportunism and vertical integration. Combining the proposed multi-level theoretical and methodological approaches, this study offers scholars a platform on which they can systematically advance their behavioral SCIM research agenda in the future.

This study investigates the dynamical evolution of travelers’ departure time choice in a bottleneck-constrained roadway system with stochastic capacity. When considering day-to-day traffic evolution, this study explores two scenarios: travelers have long-term historical knowledge and real-time information provision; and travelers have short-term travel experience and real-time information provision. In this context, this study models boundedly rational travelers. This study sheds light on the trade-off between daily (short-term) equilibrium and statistical (long-term) equilibrium.

We formulate the best response dynamic in a multitasking environment; while agents engage in multiple games concurrently, an agent can switch her action in only one of the games upon receipt of a revision opportunity. The choice of the game in which to revise an action makes the multitasking dynamic behave differently from standard evolutionary dynamics. The timing of revisions in a game becomes endogenous, which causes the transition of the action distribution in each game to depend on those in other games. Despite such complexity, we verify the global stability of the Nash equilibrium set in potential and contractive games as well as the local stability of a regular evolutionary stable state. We also show that the equilibrium to which the multitasking dynamic converges may depend on the task choice rules.

This paper studies theoretically and experimentally how success in prior interaction affects cooperation in the one-shot Inter-group Prisoner's Dilemma (IPD). We develop a model of the IPD that incorporates group-contingent social preferences and bounded rationality to derive conditions under which an increase in pro-social concerns for an out-group will increase cooperation. We then report an experiment that shows the cooperation rate increases from 8 percent in a baseline one-shot IPD to 42 percent when the IPD is preceded by a coordination game played by members of the two groups. A post-experiment survey and chat coding results using a natural language classification game both show that successful prior interaction increases individuals’ concerns for their out-group.

Although literature on open eco-innovation has recently increased, empirical research on this topic in traditional low-tech sectors is still relatively scarce. The present paper examines how open innovation activities can promote eco-innovations in the food industry, an industry that is sometimes considered a technological laggard with traditionally low cooperation. The paper focuses on the relationship between a firm's interactions with stakeholders, particularly the breadth and the depth of the firm's knowledge network and the firm's propensity to develop different types of eco-innovations. Using a novel and more accurate measure of breadth and depth and addressing endogeneity with instrumental variables, the paper studies their influence on technological eco-innovation in a random sample of 279 food firms in Spain. The results show that coordination difficulties and bounded rationality explain an inverted U shape in the relationship of breadth of external knowledge sources and the propensity to eco-innovate both in product and process eco-innovations. Additionally, our findings confirm that it is important to rely on deep, frequent and intense relationships with stakeholders in order to create the required atmosphere to foster fluent knowledge sharing among partners specially to develop eco-process innovations, but a learning effect appears. Future research should extend the analysis to other countries and sectors to address the limitations of this study.