This book brings together contributions from leading researchers in the field of agent-based modelling and simulation. This approach has grown out of some recent and innovative ideas in the social sciences, computer sciences, life sciences, physics and game theory. It is proving helpful in understanding complexity in many domains. The opportunities it offers to explore the experimental approach to social and human behaviour is proving of theoretical and empirical value across a wide range of fields. With contributions from researchers (...) whose work has served to define this new field such as Nigel Gilbert, Robert Axtell (in economics and social science) and Jacques Ferber (in multi-agent systems), as well as practitioners who are working at the cutting edge of the new domain, this collection of essays has been assembled by two of its leading exponents: Frédéric Amblard and Denis Phan. The research, case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science. (shrink)

This volume brings together contributions from leading researchers in the field of agent-based modelling and simulation. This approach has grown out of some recent and innovative ideas in the social sciences, computer sciences, life sciences, physics and game theory. It is proving helpful in understanding complexity in many domains. The opportunities it offers to explore the experimental approach to social and human behaviour is proving of theoretical and empirical value across a wide range of fields. With contributions from researchers (...) whose work has served to define this new field such as Nigel Gilbert, Robert Axtell (in economics and social science) and Jacques Ferber (in multi-agent systems), as well as practitioners who are working at the cutting edge of the new domain, this collection of essays has been assembled by two of its leading exponents: Frédéric Amblard and Denis Phan. The research, case studies and theoretical approaches discussed in this book are designed to introduce beginners and experts alike to the current state of play in this new and exciting field of social science. (shrink)

ArgumentWe examine the criteria used to validate the use of nonhuman organisms in North-American alcohol addiction research from the 1950s to the present day. We argue that this field, where the similarities between behaviors in humans and non-humans are particularly difficult to assess, has addressed questions of model validity by transforming the situatedness of non-human organisms into an experimental tool. We demonstrate that model validity does not hinge on the standardization of one type of organism in isolation, as often (...) the case with genetic model organisms. Rather, organisms are viewed as necessarily situated: they cannot be understood as a model for human behavior in isolation from their environmental conditions. Hence the environment itself is standardized as part of the modeling process; and model validity is assessed with reference to the environmental conditions under which organisms are studied. (shrink)

Linguistic Modelling of Scenarios proposes a paradigm change from the 'systemic VIEW' to 'systems SCIENCE', so as to extend the methodology of conventional science of physics into the domains hitherto beyond the reach of this kind of treatment. The book: I. Identifies the problematic issues in current approaches to the 'systemic or structural view' of parts of the world as opposed to the 'quantitative/qualitative views' of conventional science of physics and the arts whereby introducing the 'third culture'. II. Locates the (...) position of the structural view in the context of 'human intellectual endeavour'. III. Discusses the fundamental questions raised by modelling aspects of human behaviour. IV. Introduces the basic ideas and the symbolism of linguistic modelling which are then applied to turning descriptions of scenarios as a story or narrative into reasoning schemes. V. Describes a methodology of 'problem solving' of which design thinking and the operation of purposive systems are seen as essential ingredients. Problem solving is a universal activity of living in particular human beings through innovation, invention and creativity. Lack of this activity leads to death! Problem solving is regarded as pivotal point which may propel the spread of the modified structural view into social, technical, cultural and educational awareness. VI. Shows the location of aspects of conventional science within the scheme of systems science whereby achieving a 'continuity of the scientific endeavour'. VII. Outlines a teaching scheme for 'linguistic modelling'. Janos Korn explains how a view can be converted into a science which can lead to a possibility of 'organised speculation' or simulation of behaviour, exploring the effects of variation of parameters on performance, and the occurrence of outcomes of operations, beneficial or not, of dynamic structures. Static and dynamic structures are expressed in more rigorous and computable terms so that the results of analysis and design of human activity scenarios could be exposed to at least thought experiments. Linguistic Modelling of Scenarios is an informative read for any professionals, teachers and students of engineering, social science, management, business and production. (shrink)

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, (...) I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans’ belief on AI's operations and provide human-centered explanations to enhance human−AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems. (shrink)

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical (...) hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks. (shrink)

Contents: PART I BASIC ASPECTS AND VARIETIES OF CONTROL: - Emotion, Cognition, and Control: Limits of Intentionality - Self-Efficacy: The Foundation of Agency - The Orchestration of Selection, Optimization and Compensation: An Action-Theoretical Conceptualization of a Theory of Developmental Regulation - Freedom of the Will -- the Basis of Control. PART II CONSCIOUS, AUTOMATIC, AND CONTROLLED PROCESSES: - Automatic and Controlled Uses of Memory in Social Judgments - Are Controlled Processes Conscious? - Intuition and Levels of Control: The Non-Rational Way (...) of Reacting, Adapting, and Creating. PART III PERCEPTION, KNOWLEDGE, MEMORY, AND LEARNING: - The Issue of Control in Sensory and Perceptual Processes: Attention Selects and Modulates the Visual Input - The Control of Knowledge Activation in Discourse Comprehension - Working Memory and Attentional Control - Problem-Oriented Learning: Facilitating the Use of Domain-Specific and Control Strategies through Modeling by an Expert - The Role of Cognitive Structure in the Development of Behavioral Control: A Dynamic Skills Approach. (shrink)

Recent science fiction has brought anthropomorphic robots from an imaginary far-future to contemporary spacetime. Employing semiotic concepts of semiosis, unpredictability and art as a modelling system, this study demonstrates how the artificial characters in four recent series have greater analogy with human behaviour than that of machines. Through Ricoeur’s notion of identity, this research frames the films’ narratives as typical literary and thought experiments with human identity. However, the familiar sociotopes and technoscientific details included in the narratives concerning (...) data, privacy and human–machine interaction blur the boundary between the human and the machine in both fictional and real-world discourse. Additionally, utilising Haynes’ scientist stereotypes, the research puts the robot makers into focus, revealing their secret agendas and hidden agency behind the artificial creatures. (shrink)

Commentary on our target article centers around six main topics: (1) strategies in modeling the neurobehavioral foundation of human behavioral traits; (2) clarification of the construct of affiliation; (3) developmental aspects of affiliative bonding; (4) modeling disorders of affiliative reward; (5) serotonin and affiliative behavior; and (6) neural considerations. After an initial important research update in section R1, our Response is organized around these topics in the following six sections, R2 to R7.

In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and (...) self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel “iterant deformable sensorimotor medium” (IDSM), designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated body and environment and show that under certain conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor “meso-scale” between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks. (shrink)

Workers with involuntary clients influence the behaviour of their clients. One of the methods by which workers influence their clients relates to the way they model, encourage or reinforce their comments and behaviours. Practitioners may be aware or unaware of this process and of the extent to which it can impact on clients. This paper describes the process of modelling and reinforcement and discusses some of the ethical issues it raises. It suggests some guidelines by which the process may be (...) undertaken in an ethical manner and recommends the use of the ethics of care and concept of human dignity as additional theoretical resources to assist in working with this client group. (shrink)

Cognitive modeling is the effort to understand the mind by implementing theories of the mind in computer code, producing measures comparable to human behavior and mental activity. The community of cognitive modelers has traditionally met twice every 3 years at the International Conference on Cognitive Modeling. In this special issue of topiCS, we present the best papers from the ICCM meeting. These best papers represent advances in the state of the art in cognitive modeling. Since (...) ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling. (shrink)

In this essay, it is argued that naturalism of an even moderate sort speaks strongly against a certain widely held thesis about the human mental (and cognitive) architecture: that it is divided into two distinct levels, the personal and the subpersonal, about the former of which we gain knowledge in a manner that effectively insulates such knowledge from the results of scientific research. -/- An empirically motivated alternative is proposed, according to which the architecture is, so to speak, flattened (...) from above. On this flattened view, although the states and processes typically associated with the personal level likely appear in our best models of the production of human behavior, they appear alongside states and processes normally associated with the subpersonal level. Moreover, the success of such models depends nowise on a levels-based distinction between the various causal contributors. It is argued that the flattened view has methodological implications of significant import. (shrink)

This study aims to examine the underlying mechanism of the relationship between perceived green human resource management and perceived employee green behavior. By drawing on attitude and social exchange theories, we examined green commitment as a mediator and green knowledge sharing as a moderator of the GHRM–EGB relationship. The study employs partial least square structural equation modeling to analyze 329 responses. Data were collected in two time lags. The empirical results confirmed that GC mediates the relationship between (...) GHRM and EGB. However, the study results found that GKS moderated the indirect influence of GHRM on green behavior via GC. This research signifies the effect of GHRM, GKS, GC, and green behavior on organizations’ sustainability and environmental management. Despite the emerging literature on the significance of green practices in organizations for environmental management, no study has examined the moderating role of GKS on the indirect effect of GHRM on green behavior via mediating role of GC. This study offers valuable insight into environmental management in organizations through green practices and green behavior. (shrink)

The issue on the role of users in knowledge-based systems can be investigated from two aspects: the design aspect and the functionality aspect. Participatory design is an important approach for the first aspect while system adaptability supported by user modelling is crucial to the second aspect. In the article, we discuss the second aspect. We view a knowledge-based computer system as the partner of users' problem-solving process, and we argue that the system functionality can be enhanced by adapting the behaviour (...) of the system to fit the needs of users with different profiles. We emphasise that the notion of user modelling is crucial to realise such kind of flexibility. User modelling will be beneficial to the user, not only through adaptive interfaces, but also through the enhanced system adaptability. In a knowledge-based system, by incorporating user models, searching can be reduced to a smaller portion in the knowledge-base, thus enhancing system functionality. In other words, user modelling is incorporated to realise flexible inference control to achieve system adaptability. An example is provided, and a general conceptual model is sketched. We conclude this paper by emphasising that the design aspect and functionality aspect are complementary. Achieving enhanced functionality through joint efforts of computers and human users indicates a kind of participatory execution of computerised problem-solving or participatory problem-solving. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a (...) high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence (...) of linguistic conventions with both naturalistic empirical data and computational modeling. We first show conventionalization of lexicons in two different classes of naturally emerging signed systems: (a) protolinguistic “homesigns” invented by linguistically isolated Deaf individuals, and (b) a natural sign language emerging in a recently formed rich Deaf community. We find that the latter conventionalized faster than the former. Second, we model conventionalization as a population of interacting individuals who adjust their probability of sign use in response to other individuals' actual sign use, following an independently motivated model of language learning (Yang, , ). Simulations suggest that a richer social network, like that of natural (signed) languages, conventionalizes faster than a sparser social network, like that of homesign systems. We discuss our behavioral and computational results in light of other work on language emergence, and other work of behavior on complex networks. (shrink)

Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic factors. This restriction is unrealistic in light of experimental results suggesting interactions between syntax and other forms of linguistic information in human sentence processing. To address this limitation, this article introduces two sentence processing models that augment a syntactic component with information about discourse co-reference. The novel combination of probabilistic syntactic components with co-reference classifiers permits them (...) to more closely mimic human behavior than existing models. The first model uses a deep model of linguistics, based in part on probabilistic logic, allowing it to make qualitative predictions on experimental data; the second model uses shallow processing to make quantitative predictions on a broad-coverage reading-time corpus. (shrink)

The COVID-19 pandemic cropping up at the end of 2019 started to pose a threat to millions of people’s health and life after a few weeks. Nevertheless, the COVID-19 pandemic gave rise to social and economic problems that have changed the progress steps of individuals and the whole nation. In this study, the work conditions for employees from Taiwan, Malaysia, and the Chinese mainland are explored and compared, and the relationship between support mechanisms and innovation behaviors is evaluated with a (...) view of the social cognitive career theory. This study adopts the cross-sectional survey and purposive sampling to collect questionnaires. A total of 623 copies of a questionnaire from Taiwanese, 440 copies from Malaysians, and 513 copies from mainlanders were collected in this study to compare the three groups in developing employees’ IBs. Smart-partial least squares for partial least squares structural equation modeling was applied in the structural model to conduct a verification of the hypotheses and comparative analysis in this study. According to the findings, compared with employees from the Chinese mainland, the Taiwanese and Malaysian samples show more significant paths regarding employee employability, IB, prior knowledge, perceived organizational support, self-efficacy, and job performance. Our results will offer more insights and advice concerning theories of human resource. (shrink)

Psychologists have a long tradition of studying human behavior, strengths and weaknesses, biases and limitations. Economists have constructed normative frameworks that capture the most important elements of human decision-making and developed powerful tools to determine individual and strategic choices in a variety of situations. Only recently have their strengths been combined and economic models enriched with key ingredients found in psychological studies.This volume covers four of the most important themes in this interdisciplinary field: feelings, inconsistencies, limitations and (...) biases. Each chapter contributes to a more comprehensive and accurate modelling and description of human behavior. Its four parts cover: the origins, formation, and evolution of beliefs; consistency, commitment, and intertemporal separability of dynamic choices; attention, preference formation, and risk evaluation in limited cognition; and affective behaviour, specifically the role of emotions in decision making. (shrink)

Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is (...) arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at a (...) high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

Early normative studies of human behavior revealed a gap between the norms of practical rationality (what humans ought to do) and the actual human behavior (what they do). It has been suggested that, to close the gap between the descriptive and the normative, one has to revise norms of practical rationality according to the Quinean, engineering view of normativity. On this view, the norms must be designed such that they effectively account for behavior. I review (...) recent studies of human perception which pursued normative modeling and which found good agreement between the normative prescriptions and the actual behavior. I make the case that the goals and methods of this work have been incompatible with those of the engineering approach. I argue that norms of perception and action are observer-independent properties of biological agents; the norms are discovered using methods of natural sciences rather than the norms are designed to fit the observed behavior. (shrink)

This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions , at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel (...) computational model that relies on a parsimonious and flexible spatio-analogical knowledge representation structure to robustly reproduce the behavior observed with human reasoners. (shrink)

Creating android and humanoid robots to furnish companionship in the nursing care of older people continues to attract substantial development capital and research. Some people object, though, that machines of this kind furnish human–robot interaction characterized by inauthentic relationships. In particular, robotic and artificial intelligence (AI) technologies have been charged with substituting mindless mimicry of human behaviour for the real presence of conscious caring offered by human nurses. When thus viewed as deceptive, the robots also have prompted (...) corresponding concerns regarding their potential psychological, moral, and spiritual implications for people who will be interacting socially with these machines. The foregoing objections and concerns can be assessed quite differently, depending upon ambient religious beliefs or metaphysical presuppositions. The complaints may be set aside as unnecessary, for example, within religious traditions for which even current robots can be viewed as presenting spiritual aspects. Elsewhere, technological cultures may reject the complaints as expression of outdated superstition, holding that the machines eventually will enjoy a consciousness described entirely in materialist and behaviourist terms. While recognizing such assessments, the authors of this essay propose that the heart of the foregoing objections and concerns may be evaluated, in part, scientifically – albeit with a conclusion recommending fundamental revisions in AI modelling of human mental life. Specifically, considerations now favour introduction of AI models using interactive classical and quantum computation. Without this change, the answer to the essay's title question arguably is ‘no’ – with it, the answer plausibly becomes ‘maybe’. Either outcome holds very interesting implications for nurses. (shrink)

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human (...) categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. (shrink)

In recent years, much work has been dedicated by logicians, computer scientists and economists to understanding awareness, as its importance for human behaviour becomes evident. Although several logics of awareness have been proposed, little attention has been explicitly dedicated to change in awareness. However, one of the most crucial aspects of awareness is the changes it undergoes, which have countless important consequences for knowledge and action. The aim of this paper is to propose a formal model of awareness change, (...) and to derive from it logics of awareness change. In the first part of the paper, the model of epistemic states of bounded agents proposed in Hill (Stud Log 89(1):81–109, 2008a ) is extended with operations modelling awareness change. In the second part of the paper, it is shown how this model naturally extends the “standard” logic of awareness to yield a logic of awareness change. (shrink)

Despite the success of artificial intelligence, we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference (...) to combine intuitive models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand. (shrink)

Building on and partially refining previous theoretical work, this paper presents an extended simulation model of ancestral warfare. This model (1) disentangles attack and defense, (2) tries to differentiate more strictly between selfish and altruistic efforts during war, (3) incorporates risk aversion and deterrence, and (4) pays special attention to the role of brutality. Modeling refinements and simulation results yield a differentiated picture of possible evolutionary dynamics. The main observations are: (i) Altruism in this model is more likely to (...) evolve for defenses than for attacks. (ii) Risk aversion, deterrence and the interplay of migration levels and brutality can change evolutionary dynamics substantially. (iii) Unexpectedly, one occasional simulation outcome is a dynamically stable state of ‘tolerated intergroup theft’, raising the question if corresponding patterns also exist in real intergroup conflicts. Finally, possible implications for theories of the co-evolution of bellicosity and altruism in humans are discussed. (shrink)

Background To produce and understand words, humans access the mental lexicon. From a functional perspective, the long-term memory component of the mental lexicon is comprised of three levels: the concept level, the lemma level, and the phonological level. At each level, different kinds of word information are stored. Semantic as well as phonological cues can help to facilitate word access during a naming task, especially when neural dysfunctions are present. The processing corresponding to word access occurs in specific parts of (...) working memory. Neural models for simulating speech processing help to uncover the complex relationships that exist between neural dysfunctions and corresponding behavioral patterns. Methods The Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA) are used to develop a quantitative neural model of the mental lexicon and its access during speech processing. By simulating a picture-naming task (WWT 6-10), the influence of cues is investigated by introducing neural dysfunctions within the neural model at different levels of the mental lexicon. Results First, the neural model is able to simulate the test behavior for normal children that exhibit no lexical dysfunction. Second, the model shows worse results in test performance as larger degrees of dysfunction are introduced. Third, if the severity of dysfunction is not too high, phonological and semantic cues are observed to lead to an increase in the number of correctly named words. Phonological cues are observed to be more effective than semantic cues. Conclusion Our simulation results are in line with human experimental data. Specifically, phonological cues seem not only to activate phonologically similar items within the phonological level. Moreover, phonological cues support higher-level processing during access of the mental lexicon. Thus, the neural model introduced in this paper offers a promising approach to modeling the mental lexicon, and to incorporating the mental lexicon into a complex model of language processing. (shrink)

Quantum theory is a powerful framework for probabilistic modeling of cognition. Strong empirical evidence suggests the context- and order-dependent representation of human judgment and decision-making processes, which falls beyond the scope of classical Bayesian probability theories. However, considering behavior as the output of underlying neurobiological processes, a fundamental question remains unanswered: Is cognition a probabilistic process at all?

Individuals make decisions under uncertainty every day. Decisions are based on in- complete information concerning the potential outcome or the predicted likelihood with which events occur. In addition, individuals’ choices often deviate from the rational or mathematically objective solution. Accordingly, the dynamics of human decision making are difficult to capture using conventional, linear mathematical models. Here, we present data from a 2-choice task with variable risk between sure loss and risky loss to illustrate how a simple nonlinear dynamical system (...) can be employed to capture the dynamics of human decision making under uncertainty (i.e., multistability, bifurcations). We test the feasibility of this model quantitatively and demonstrate how the model can account for up to 86% of the observed choice behavior. The implications of using dynamical models for explaining the nonlin- ear complexities of human decision making are discussed as well as the degree to which the theory of nonlinear dynamical systems might offer an alternative framework for understanding human decision making processes. (shrink)

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive (...) steps: (1) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT-R (adaptive control of thought - rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT-R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT-R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior. (shrink)

With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM). In this paper, we introduce the inner loop of human–machine teaming expressed as communication with MToM capability. We present three different approaches to MToM: (1) constructing models of human inference with well-validated psychological theories and empirical measurements; (...) (2) modeling human as a copy of the AI; and (3) incorporating well-documented domain knowledge about human behavior into the above two approaches. We offer a formal language for machine communication and MToM, where each term has a clear mechanistic interpretation. We exemplify the overarching formalism and the specific approaches in two concrete example scenarios. Related work that demonstrates these approaches is highlighted along the way. The formalism, examples, and empirical support provide a holistic picture of the inner loop of human–machine teaming as a foundational building block of collective human–machine intelligence. (shrink)

In this paper we compare two micro foundations for modelling human behaviour and decision making. We focus on perfect strategic rationality on the one hand and a simple reinforcement mechanism on the other hand. Iterated prisoner’s dilemmas serve as the play ground for the comparison. The main lesson of our analysis is that in the space of all possible 2 × 2 PDs different micro foundations do matter. This suggests that researchers can not safely rely on the assumption that (...) implementing simple models of decision making will yield the same results that may be obtained when more sophisticated decision rules are built into the agents. (shrink)

When game theory was introduced to biology, the components of classic game theory models were replaced with elements more befitting evolutionary phenomena. The actions of intelligent agents are replaced by phenotypic traits; utility is replaced by fitness; rational deliberation is replaced by natural selection. In this paper, I argue that this classic conception of comprehensive reapplication is misleading, for it overemphasizes the discontinuity between human behavior and evolved traits. Explicitly considering the representational roles of evolutionary game theory brings (...) to attention neglected areas of overlap, as well as a range of evolutionary possibilities that are often overlooked. The clarifications this analysis provides are well-illustrated by—and particularly valuable for—game theoretic treatments of the evolution of social behavior. (shrink)

Intellectualists about knowledge how argue that knowing how to do something is knowing the content of a proposition (i.e, a fact). An important component of this view is the idea that propositional knowledge is translated into behavior when it is presented to the mind in a peculiarly practical way. Until recently, however, intellectualists have not said much about what it means for propositional knowledge to be entertained under thought's practical guise. Carlotta Pavese fills this gap in the intellectualist view (...) by modeling practical modes of thought after Fregean senses. In this paper, I take up her model and the presuppositions it is built upon, arguing that her view of practical thought is not positioned to account for much of what human agents are able to do. (shrink)

Based on an integrated theoretical framework, this study analyzes user acceptance behavior toward socially interactive robots focusing on the variables that influence the users’ attitudes and intentions to adopt robots. Individuals’ responses to questions about attitude and intention to use robots were collected and analyzed according to different factors modified from a variety of theories. The results of the proposed model explain that social presence is key to the behavioral intention to accept social robots. The proposed model shows the (...) significant roles of perceived adaptivity and sociability, both of which affect attitude as well as influence perceived usefulness and perceived enjoyment, respectively. These factors can be key features of users’ expectations of social robots, which can give practical implications for designing and developing meaningful social interaction between robots and humans. The new set of variables is specific to social robots, acting as factors that enhance attitudes and behavioral intentions in human–robot interactions. Keywords: Robot acceptance model; Socially interactive robots; Social robots; Social presence. (shrink)

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key (...) property characteristic of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)

The specific regions in the brain that are active when some behaviour is observed is a kind of information that may be interesting for neuroscientists, but how could it be fruitful for economic theory? The thesis defended in the essay is that the brain matters to prediction. By using the Ultimatum Game as a benchmark, it is argued that if the goal of a model of human behaviour is to yield good predictions about important classes of choices, then models (...) that incorporate neurobiological variables may have some advantages over alternative models.The essay includes two parts. Part I first analyses the Ultimatum Game and illustrates some of its experimental results. Then, it evaluates in detail the merits and shortcomings of Cristina Bicchieri’s model based on social norms. It centres on the predictive power of the model, and articulates some challenges it faces. Part II begins with a review of some neurobiological findings which suggest a different approach to construct predictive models of human behaviour. Drawing upon these findings, it gives some reasons why the predictions of a neurobiologically‐informed model have some advantage over those of Bicchieri’s model. A critical discussion of the thesis against possible objections terminates the essay. The conclusion follows that one way in which the study of the neurobiological foundations of decision‐making might be fruitful for economic modelling is in enhancing the predictive quality of its models. (shrink)

Context: In previous papers, I suggested six rules proposed by Varela, Maturana and Uribe as a validation test to assess the autopoietic nature of a complex dynamic system. Identifying possible non-biological autopoietic systems is harder than merely assessing self-organization, existence of embodied boundaries and some observable autonomous behavioural capabilities: any rigorous assessment should include a close observation of the “intra-boundaries” phenomenology in terms of components’ self-production, their spatial distribution and the temporal occurrence of interaction events. Problem: Under which physical and (...) components’ relational conditions can some social systems be properly considered as autopoietic unities compliant with the six rules? Results: Dynamic systems can be classified according to “degrees of autonomous behaviour” that they may acquire as a result of the emergence of organizational closure (i.e., autonomy. Also, the different “degrees of attainable systemic autonomy” depend on the “degrees of autonomy” shown by a system’s dynamic components. For human social systems, a necessary balance between individuals’ autonomy and the heteronomous behaviour brought about on people by social norms (laws, culture, tradition or coercion) sets limits to the “degree of systemic autonomy” that human organizations may acquire. Therefore social systems, defined as dynamic systems composed of physical agents, could not attain the high “levels of systemic autonomy” ascribable to autopoietic systems without constraining the autonomy of agents to “levels” that are incompatible with spontaneous human behaviour. Also, social organizations seen as composed of physical agents interacting in physical space cannot be construed as autopoietic systems. Alternatively, if seen as composed of “process-like” entities, where agents participate as actors within processes, some social systems could be described as autopoietic wholes existing in the abstract space in which we distinguish interactions between processes, provided that we can assess compliance with the rules for some specific cases. Implications: These conclusions contribute to the debate on the possible autopoietic nature of some human social systems and to grasping the opportunity to shift focus to the more interesting issue of the “degrees of systemic autonomy” that human organizations could acquire (if needed) without imposing unbearable limitations on the autonomy of human actors. Also, the conceptual framework of this explanatory approach could be used in practical terms to assist the development of new dynamic modelling languages capable of simulating social systems. (shrink)

As a full-blown research topic, numerical cognition is investigated by a variety of disciplines including cognitive science, developmental and educational psychology, linguistics, anthropology and, more recently, biology and neuroscience. However, despite the great progress achieved by such a broad and diversified scientific inquiry, we are still lacking a comprehensive theory that could explain how numerical concepts are learned by the human brain. In this perspective, I argue that computer simulation should have a primary role in filling this gap because (...) it allows identifying the finer-grained computational mechanisms underlying complex behavior and cognition. Modeling efforts will be most effective if carried out at cross-disciplinary intersections, as attested by the recent success in simulating human cognition using techniques developed in the fields of artificial intelligence and machine learning. In this respect, deep learning models have provided valuable insights into our most basic quantification abilities, showing how numerosity perception could emerge in multi-layered neural networks that learn the statistical structure of their visual environment. Nevertheless, this modeling approach has not yet scaled to more sophisticated cognitive skills that are foundational to higher-level mathematical thinking, such as those involving the use of symbolic numbers and arithmetic principles. I will discuss promising directions to push deep learning into this uncharted territory. If successful, such endeavor would allow simulating the acquisition of numerical concepts in its full complexity, guiding empirical investigation on the richest soil and possibly offering far-reaching implications for educational practice. (shrink)

Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or Bayes nets, can (...) simulate rational belief updating. When fit to experimental data, Bayes nets can help identify the factors that contribute to polarization. We present a study into belief updating concerning the reality of climate change in response to information about the scientific consensus on anthropogenic global warming. The study used representative samples of Australian and U.S. participants. Among Australians, consensus information partially neutralized the influence of worldview, with free-market supporters showing a greater increase in acceptance of human-caused global warming relative to free-market opponents. In contrast, while consensus information overall had a positive effect on perceived consensus among U.S. participants, there was a reduction in perceived consensus and acceptance of human-caused global warming for strong supporters of unregulated free markets. Fitting a Bayes net model to the data indicated that under a Bayesian framework, free-market support is a significant driver of beliefs about climate change and trust in climate scientists. Further, active distrust of climate scientists among a small number of U.S. conservatives drives contrary updating in response to consensus information among this particular group. (shrink)

Griffiths, Christian, and Kalish (this issue) present an iterative‐learning paradigm applying a Bayesian model to understand inductive biases in categorization. The authors argue that the paradigm is useful as an exploratory tool to understand inductive biases in situations where little is known about the task. It is argued that a theory developed only at the computational level is much like a single‐bladed knife that is only useful in highly idealized situations. To be useful as a general tool that cuts through (...) the complex fabric of cognition, we need at least two‐bladed scissors that combine both computational and psychological constraints to characterize human behavior. To temper its sometimes expansive claims, it is time to show what a Bayesian model cannot explain. Insight as to how human reality may differ from the Bayesian predictions may shed more light on human cognition than the simpler focus on what the Bayesian approach can explain. There remains much to be done in terms of integrating Bayesian approaches and other approaches in modeling human cognition. (shrink)

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule‐based models that often produce brittle behavior. Rule‐based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task‐specific rules from instructions and experience, and that bottom‐up processing is used whenever possible. In a model of learning perfect time‐sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom‐up activation of instructions can (...) explain parallel behavior. In a model of a complex dynamic task (Carnegie Mellon University Aegis Simulation Program [CMU‐ASP], Anderson et al., 2004), parallel behavior is explained by the transition from serially organized instructions to rules that are activated by both top‐down (goal‐driven) and bottom‐up (perceptually driven) factors. Parallelism lets the model opportunistically reorder instructions, leading to the gradual emergence of new task strategies. (shrink)

The redundant-signals paradigm (RSP) is designed to investigate response behavior in perceptual tasks in which response-relevant targets are defined by either one or two features, or modalities. The common finding is that responses are speeded for redundantly compared to singly defined targets. This redundant-signals effect (RSE) can be accounted for by race models if the response times do not violate the race model inequality (RMI). When there are violations of the RMI, race models are effectively excluded as a viable (...) account of the RSE. The common alternative is provided by co-activation accounts, which assume that redundant target signals are integrated at some processing stage. However, ‘co-activation’ has mostly been only indirectly inferred and the accounts have only rarely been explicitly modeled; if they were modeled, the RSE has typically been assumed to have a decisional locus. Yet, there are also indications in the literature that the RSE might originate, at least in part, at a non-decisional or motor stage. In the present study, using a distribution analysis of sequential-sampling models (ex-Wald and Ratcliff Diffusion model), the locus of the RSE was investigated for two bimodal (audio-visual) detection tasks that strongly violated the RMI, indicative of substantial co-activation. Three model variants assuming different loci of the RSE were fitted to the quantile reaction time proportions: a decision, a non-decision, and a combined variant both to vincentized group as well as individual data. The results suggest that for the two bimodal detection tasks, co-activation has a shared decisional and non-decisional locus. These findings point to the possibility that the mechanisms underlying the RSE depend on the specifics (task, stimulus, conditions, etc.) of the experimental paradigm. (shrink)

Computer modelling of personality and behaviour is becoming increasingly important in many fields of computer science and psychology. Personality and emotion-driven Believable Agents are needed in areas like human–machine interfaces, electronic advertising and, most notably, electronic entertainment. Computer models of personality can help explain personality by illustrating its underlying structure and dynamics. This work presents a neural network model of personality and personality change. The goals are to help understand personality and create more realistic and believable characters for interactive (...) video games. The model is based largely on trait theories of personality. Behaviour in the model results from the interaction of three components: personality-based predispositions for behaviour, moods/emotions and environmental situations. Personality develops gradually over time depending on the situations encountered. Modelling personality change produces interesting and believable virtual characters whose behaviours change in psychologically plausible ways. (shrink)

The approach used by physics is based on the identification and study of ideal objects, which is also the basis of biophysics, in combination with von Neumann heuristic modeling and functional fractionation according to R.Rosen is discussed as a tool for studying the properties of consciousness. The object of the study is a kind of line of analog systems: the human brain, the vertebrate brain, the invertebrate brain and artificial neural networks capable of reflection, which is a key (...) property characteristic of consciousness. Reflection in the broad sense of the word, understood as an internal representation of the external world, is characteristic of a wide range of animals, and some of them (bumblebees, fish) even demonstrate reflection in the narrow sense of the word, understood as an inner self-representation. This complex behavior is realized by miniature brains of ~1 million neurons. The use of simple recurrent neural networks (RNNs) to obtain answers to general questions is illustrated. For example, it has been shown a small RNS is able to pass delayed matching to sample (DMTS) test, forming an individual dynamic representation of the received stimulus, allowing decoding by a special external neural detector.. It has been demonstrated in the reflexive game “even-odd”, the RNS has a huge advantage over a multi-layered neural network, with the same and a larger number of neurons – reflection defeats regression. It was found that the asymmetry of outcomes in the odd-even game, which was explained by various causes, including psychological ones – “it’s easier to catch up than to run away”, is reproduced in the game of two RNNs. Obviously, there are no psychological causes here and the advantage of the player playing for “even” is explained by the more complex strategy of the “odd” player – he needs to predict the opponent’s move and choose the opposite one. (shrink)