Behavioral Macroeconomics: Integrating Psychology into Economic Models

Many of today's large scale systems require dynamic coordination among distributed decisionmakers. A crucial element in the coordination involves the use of resources which have finite setup times and are constrained by their effective durations. The presence of these constraints may induce human cognitive limitations and biases and lead to coordination failure. This paper presents a quantitative methodology for identifying the impact of resource constraints on distributed team coordination in a dynamic and uncertain task processing environment. The approach uses Petri net as the formalism to model team decisionmaking process. By incorporating rational decisionmaking strategies and recognizable human limitations, various resource and task conditions are simulated. Hypotheses on the impact of resource constraints are then generated based on simulation results, and a distributed human team task processing experiment has been designed and conducted to help validate the hypotheses. Experimental data are currently being analyzed, and model-data comparison will help examine the impact of the resource constraints on human team decisionmaking and coordination processes, and identify human cognitive limitations and biases.

Large Language Models (LLMs) demonstrate impressive capabilities across diverse tasks, yet their safety mechanisms remain susceptible to adversarial exploitation of cognitive biases---systematic deviations from rational judgment. Unlike prior studies focusing on isolated biases, this work highlights the overlooked power of multi-bias interactions in undermining LLM safeguards. Specifically, we propose CognitiveAttack, a novel red-teaming framework that adaptively selects optimal ensembles from 154 human social psychology-defined cognitive biases, engineering them into adversarial prompts to effectively compromise LLM safety mechanisms. Experimental results reveal systemic vulnerabilities across 30 mainstream LLMs, particularly open-source variants. CognitiveAttack achieves a substantially higher attack success rate than the SOTA black-box method PAP (60.1% vs. 31.6%), exposing critical limitations in current defenses. Through quantitative analysis of successful jailbreaks, we further identify vulnerability patterns in safety-aligned LLMs under synergistic cognitive biases, validating multi-bias interactions as a potent yet underexplored attack vector. This work introduces a novel interdisciplinary perspective by bridging cognitive science and LLM safety, paving the way for more robust and human-aligned AI systems.

Combining conscious and unconscious knowledge within human-machine-interfaces to foster sustainability with decision-making concerning production processes

Virtual work opened a whole lot of change when it came to the cybersecurity landscape. Technology has made many contributions to secure remote workplaces, but the human element has always been a targeted attack vector to mitigate cyber threats. This paper explores the influence of human behavior and cognitive biases on the vulnerabilities that affect remote work environments due to poor security decisions coming because of some particular biases and how the features of remote work amplify their effects. The paper also suggests appropriate techniques for organizations on how to tackle these issues through effective training, awareness programs, and by improving security policies. Understanding and mitigating the human factors involved in Cybersecurity would give organizations an edge in developing more secure and resilient remote work environments.

The Behavioural Economics of Cybersecurity, Emphasizing the Role of Human Behaviour, Cultural Influences, and Cognitive Biases in Shaping Cybersecurity Vulnerabilities and Solutions.

Task. Risk management is a structured approach to identifying, assessing and minimizing risks that may affect the achievement of an organization's goals. The subject of the study is the methodology of risk management of an organization. The object of the study is the risk management system of an organization. The problems are mainly related to the fact that risk management is often based on incomplete or outdated data, which makes it difficult to accurately assess risks. Assessing the probability and consequences of risks can be subjective and depend on the opinion of experts, which can lead to erroneous conclusions. Many methodologies do not take into account uncertainty and volatility, which can lead to underestimation of risks. Risk management is sometimes not integrated into the overall strategy of the company, which makes it less effective. At the same time, new technologies, economic conditions or legislative changes can affect the outcomes. Also, the human factor and cognitive biases can affect the perception and management of risks, for example, ignoring rare but catastrophic events, and the negative influence of management culture, manifested in the lack of encouragement for open discussion of risks or the use of tools to reduce them, negatively affects the results of the organization. Model. To substantiate the methodological aspects of forming the organization's risk management in accordance with the standards. The theoretical and methodological basis of the study was the works of leading domestic and foreign scientists in the field of theory and methodology of risk management of organizations. In the course of the study, a set of general scientific and special methods was used. Analysis and synthesis were used to identify the essential characteristics and components of the risk management system. Conclusions. The methodology of forming the organization's risk management in accordance with the management standards is of theoretical significance in terms of developing scientific and theoretical provisions concerning the construction of a risk management system and improving its efficiency. Practical significance. The practical significance of the study lies in the formation of applied recommendations for forming the risk management system in the context of increasing the balance and adaptability of the organization's strategic priorities to changes in the internal and external environment. The developed methodological approaches to forming risk management, taking into account the parameters of the organization's risk sustainability, are aimed at improving the efficiency of implementing the results of activities in a positive way. Originality. Methodological aspects of forming the organization's risk management in adaptation to modern conditions of economic processes development.

Think about Richard Scarry’s Cars and Trucks and Things That Go. Think about what that book would have looked like in sequential decades of the last century had Richard Scarry been alive in each of them to delight and amuse children and parents. Each subsequent decade has seen the development of ever more specialized vehicles. We started with the Model T Ford. We now have more models of backhoe loaders than even the most precocious fouryear-old can identify. What relevance does this have for economics? In the late 1960’s there was a shift in the job description of economic theorists. Prior to that time microeconomic theory was mainly concerned with analyzing the purely competitive, general-equilibrium model based upon profit maximization by firms and utility maximization by consumers. The macroeconomics of the day, the so-called neoclassical synthesis, appended a fixed money wage to such a generalequilibrium system. “Sticky money wages” explained departures from full employment and business-cycle fluctuations. Since that time, both microand macroeconomics have developed a Scarry-ful book of models designed to incorporate into economic theory a whole variety of realistic behaviors. For example, “The Market for ‘Lemons’ ” explored how markets with asymmetric information operate. Buyers and sellers commonly possess different, not identical, information. My paper examined the pathologies that may develop under these more realistic conditions. For me, the study of asymmetric information was a very first step toward the realization of a dream. That dream was the development of a behavioral macroeconomics in the original spirit of John Maynard Keynes’ General Theory (1936). Macroeconomics would then no longer suffer from the “ad hockery” of the neoclassical synthesis, which had overridden the emphasis in The General Theory on the role of psychological and sociological factors, such as cognitive bias, reciprocity, fairness, herding, and social status. My dream was to strengthen macroeconomic theory by incorporating assumptions honed to the observation of such behavior. A team of people has participated in the realization of this dream. Kurt Vonnegut would call this team a kerass, “a group of people who are unknowingly working together toward some common goal fostered by a larger cosmic influence.” In this lecture I shall describe some of the behavioral models developed by this kerass to provide plausible explanations for macroeconomic phenomena which are central to Keynesian economics. For the sake of background, let me take you back a bit in time to review some history of macroeconomic thought. In the late 1960’s the New Classical economists saw the same weaknesses in the microfoundations of macroeconomics that have motivated me. They hated its lack of rigor. And they sacked it. They then held a celebratory bonfire, with an article entitled “After Keynesian Macroeconomics.” The new version of macroeconomics that they produced became standard in the 1970’s. Following its neoclassical synthesis predecessor, New Classical macroeconomics was based on the competitive, general-equilibrium model. But it differed in being much more zealous in insisting that all decisions—consumption and labor supply by † This article is a revised version of the lecture George A. Akerlof delivered in Stockholm, Sweden, on December 8, 2001, when he received the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel. The article is copyright © The Nobel Foundation 2001 and is published here with the permission of the Nobel Foundation.

In traditional economic models, individual utility depends only on absolute consumption. These models lie at the heart of claims that pursuit of individual self-interest promotes aggregate welfare. Recent years have seen renewed interest in economic models in which individual utility depends not only on absolute consumption, but also on relative consumption. In contrast to traditional models, these models identify a fundamental conflict between individual and social welfare. The conflict stems from the fact that concerns about relative consumption are stronger in some domains than in others. The disparity gives rise to expenditure arms races focused on positional goods—those for which relative position matters most. The result is to divert resources from nonpositional goods, causing welfare losses. Compelling theoretical and empirical evidence confirms the importance of relative consumption in individual valuations. In light of this evidence, we must question the wisdom of economic policy recommendations stemming from models that ignore relative consumption.

Enlargement of the scope of human activities and the improvement of the ability to transform nature are posing severe challenges to the environmental carrying capacity of the entire planet. Earth's environmental resource systems are facing systemic risks that cannot be evaded. Environmental crises have become a bottleneck restricting economic and social development. Resources are most likely to become the most important strategic resources. The social production methods and operating mechanisms under the traditional economic model are bound to be challenged, as the era of ecological civilization is approaching. On the basis of fully respecting the laws of nature and society, this paper uses mechanism design theory to design environmental resources including environmental coordination and assessment mechanisms, market trading mechanisms, macro-control mechanisms, scientific innovation mechanisms, and innovative talent introduction mechanisms. Conclusions and suggestions are rendered accordingly to use mechanisms to resolve the contradiction between economic development and environmental protection, aiming to coordinate relevant conflicts of interest.

Wicked problems in environmental economics refer to complex, multifaceted issues that defy straightforward solutions due to the interplay of social, economic, and environmental factors. These problems are often characterized by uncertainty, competing values, and dynamic conditions, making them challenging to address through traditional economic models or policy approaches. One of the key characteristics of wicked problems is that they lack a clear, definitive solution. Unlike “tame” problems, where outcomes can be predicted and optimized, wicked problems involve multiple stakeholders with diverse interests and values. For example, climate change, biodiversity loss, and water resource management are all wicked problems because they affect various sectors—agriculture, energy, and ecosystems—each with their own priorities. What benefits one group may harm another, creating conflicts over the best course of action. Another complicating factor is the high level of uncertainty associated with wicked problems. Scientific data and economic models can help predict potential outcomes, but they often cannot capture the full scope of these issues, especially when considering long-term environmental impacts. In environmental economics, this uncertainty is exacerbated by the interdependence of ecological systems and human activities. Wicked problems are also dynamic, meaning they evolve over time as new information becomes available and as social, economic, and environmental conditions change. Finally, the resolution of wicked problems often involves trade-offs between competing goals, such as economic growth and environmental protection. These trade-offs are inherently political, as different stakeholders—governments, industries, communities, and advocacy groups—often have divergent views on what should be prioritized. This complicates the policy process, requiring negotiation and collaboration across sectors.

Tax compliance is a behavior which is not driven only by the threats of penalties and punishments, but one which tends to be influenced by the economic and societal factors in the taxpayer's environment as well as the taxpayer's own psychology. To understand any human behavior, an interdisciplinary approach is preferred as behavior is acknowledged to be the result of the complex interaction of internal aspects of being human like emotions, cognitive processes along with factors from the external environment like social norms and uncertainty. This chapter aims to provide a comprehensive and interdisciplinary explanation of tax compliance behavior based on the theories grounded in behavioral economics and which encompass the influence of emotions, cognitive biases, social preferences and neuroscience. The empirical evidence from behavioral and neuroeconomic research discussed provides a more nuanced and comprehensive understanding of a taxpayer's decision making process and provides examples that can be used by tax administrations in any country to improve tax compliance behavior.

The rising prevalence of harassment in Middle Eastern countries is mirrored in literary works from the region. However, extracting data from these texts to understand the typology and frequency of the cases poses a significant challenge due to human cognitive limitations and potential biases. Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The data source for this study consists of twelve Middle Eastern novels. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model. Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis. Finally, a long short-term memory-gated recurrent unit (LSTM-GRU) deep learning model is built to classify the sentiment characteristics that induce sexual harassment. The proposed model achieved an accuracy of 75.8% while outperforming five other algorithms. Additionally, a sentiment classification with three labels—negative, positive, and neutral—was developed using an LSTM-GRU RNN deep learning model. The accuracy of this model was 84.5%. Most statements, even those involving physical sexual harassment, which had greater levels of sexual harassment, had negative sentiments, according to lexicon-based sentiment analysis. This study contributes to the field of text mining by providing a novel approach to identifying instances of sexual harassment in literature in English from the Middle East. The use of machine learning models and sentiment analysis techniques allows for more accurate identification and classification of different types of sexual harassment. Furthermore, this study sheds light on the prevalence of sexual harassment in Middle Eastern countries and highlights the need for further research and action to address this issue.

The rapid development of artificial intelligence (AI) technology is profoundly transforming economic management models. This paper explores the applications of AI in economic forecasting, decision optimization, supply chain management, fintech, and human resource management, and analyzes its challenges and reshaping effects on traditional economic management models. The study shows that AI improves the efficiency and accuracy of economic management through big data analysis, machine learning, and automated decision-making, but also brings challenges such as data security, ethical issues, and employment structure adjustments. Combining existing literature and case studies, this paper proposes future development trends of economic management models and suggests that policymakers and business managers actively adapt to the changes brought by AI technology while strengthening regulation and ethical standards.

Error management theory (EMT; Haselton & Buss, 2000) draws on parental investment theory (Trivers, 1972) and signal detection to make novel predictions about human cognitive biases and their adaptive implications. EMT predicts that heterosexual men overperceive sexual interest from women, whereas women underperceive honest signals of relationship commitment from men. In turn, sexual strategies theory (Buss & Schmitt, 1993) predicts that men may experience more regret over romantic or sexual omission (missed opportunities), whereas women may experience more regret over romantic or sexual commission (regretting past decisions). We tested these predictions using craigslist's missed connections (personal ads posted by people on craigslist.org seeking to contact someone they saw briefly in public) and FMyLife.com's (FML) love and intimacy sections (embarrassing incidents that people experience and choose to share online anonymously). We recorded missed connections for men seeking women and women seeking men in all 50 U.S. states at 3 time points (N > 61,000). We also recorded FMLs posted by men and women over a 3-year span (N > 3,500). Consistent with EMT, parental investment theory, and sexual strategies theory, men were more likely to post missed connections (sexual or romantic omission regret), whereas women were more likely to post in FML's love and intimacy sections (sexual or romantic commission regret). We discuss EMT's broad theoretical implications for psychology.

PurposeLimited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.Design/methodology/approachA Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.FindingsResults of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.Originality/valueThis paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.