Suchergebnisse

Intro -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction: Toward behavioral computational social science -- 1.1 Research strategies in CSS -- 1.2 Why behavioral CSS -- 1.3 Organization of the book -- PART I CONCEPTS AND METHODS -- Chapter 2 Explanation in computational social science -- 2.1 Concepts -- 2.1.1 Causality -- 2.1.2 Data -- 2.2 Methods -- 2.2.1 ABMs -- 2.2.2 Statistical mechanics, system dynamics, and cellular automata -- 2.3 Tools -- 2.4 Critical issues: Uncertainty, model communication -- Chapter 3 Observation and explanation in behavioral sciences -- 3.1 Concepts -- 3.2 Observation methods -- 3.2.1 Naturalistic observation and case studies -- 3.2.2 Surveys -- 3.2.3 Experiments and quasiexperiments -- 3.3 Tools -- 3.4 Critical issues: Induced responses, external validity, and replicability -- Chapter 4 Reasons for integration -- 4.1 The perspective of agent-based modelers -- 4.2 The perspective of behavioral social scientists -- 4.3 The perspective of social sciences in general -- PART II BEHAVIORAL COMPUTATIONAL SOCIAL SCIENCE IN PRACTICE -- Chapter 5 Behavioral agents -- 5.1 Measurement scales of data -- 5.2 Model calibration -- 5.2.1 Single decision variable and simple decision function -- 5.2.2 Multiple decision variables and multilevel decision trees -- 5.3 Model classification -- 5.4 Critical issues: Validation, uncertainty modeling -- Chapter 6 Sophisticated agents -- 6.1 Common features of sophisticated agents -- 6.2 Cognitive processes -- 6.2.1 Reinforcement learning -- 6.2.2 Other models of bounded rationality -- 6.2.3 Nature-inspired algorithms -- 6.3 Cognitive structures -- 6.3.1 Middle-level structures -- 6.3.2 Rich cognitive models -- 6.4 Critical issues: Calibration, validation, robustness, social interface -- Chapter 7 Social networks and other interaction structures.

AbstractThis double interview with two distinguished researchers in computational neuroscience, Kanaka Rajan and Alessandro Treves, aims to capture a part of their talks and discussions that emerged during a workshop on physical modelling of thought, held in Berlin in January 2023. The topic is the fascinating all-round intersection of physics and neuroscience through the perspectives of the interviewees. The dialogue traverses the complex terrain of modelling thought processes, shedding light on the trade-off between simplicity and complexity that defines the field of computational neuroscience. From the early days of physics-inspired brain models to the cutting-edge advancements in large language models, the interviewees share their journey, challenges, and insights into the modelling of physical and biological systems; they recount their experience with computational neuroscience, explore the impact of large language models on our understanding of human language and cognition, and speculate on the future directions of physics-inspired computational neuroscience, emphasising the importance of interdisciplinary collaboration and a deeper integration of complexity and detail in modelling the brain and its functions.

This article sets out our perspective on how to begin the journey of decolonising computational fi elds, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour; and b) rejection of the idea that centring individual people is a solution to system-level problems. The longer we ignore these two steps, the more "our" academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fi elds' histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as "diversity boards", which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences — including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artifi cial intelligence. We aspire to progress away fromthese fi elds' stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurturesdemographically diverse researchers and ideas that critically challenge the status quo.