Economics

Addressing the policy challenges and opportunities of “Big data”

Social change is understood as a more or less rapid and comprehensive change of societal structures and institutions, including changes to the economic, technological, and cultural frameworks of a society (Calhoun, 1992). A case in point is the breakup or transformation of the former communist countries in Europe in the 1980s. The political transition to representative democracy included a profound change of the economic system to market capitalism, which was soon overlaid by the negative effects of globalization. Today, some 20 years after the event, we have a huge variation of political and economic transformations around the world. These transformations also include countries such as the United Kingdom or China that have undergone economic reforms without changes in the basics of the political system. Given that social change is evident at the macro level, it is also plausible to assume that such changes have an impact on individuals’ behavior and development. Until recently, however, such issues did not rank very high on the research agenda, at least not for psychology. This is the background to this Special Section, which brings together papers that investigate the effect of social change on the lives of individuals.

What is Behavioral Economics?

Big Data, Big Expectations, and Big Judgements.

This symposium highlights the transformative interplay between control and trust dynamics in the age of AI and Big Data by integrating insights across macro, meso, and micro levels of analysis. Juxtaposing these insights in the discussion facilitated by Sim Sitkin, the symposium reveals a comprehensive understanding of how technological advancements redefine control and trust dynamics inherent in managerial practices, organizational structures, and broader societal governance. On a more macro level, Paul Adler examines how societal governance structures influence the trajectory of AI development, that is how governments and societal collectives engage with and control AI development to maximize benefits and mitigate risks, providing insights into the intersection of societal control, trust, and technological evolution. At the organizational level, Markus Kreutzer and Jorge Walter focus on algorithmic supervision and human-AI teams (HATs), exploring how these configurations influence control-trust dynamics within modern organizations and, thus, provide a detailed understanding of how AI-enabled controls shape trust among those being controlled and those implementing the controls. On the more meso/micro-level Mareike Möhlmann and colleagues adopt a grounded theory approach on how YouTube as a platform organization controls conflicts (e.g., freedom of speech, content diversity, and safety) while fostering trust and performance through human-machine collaboration. Finally, Kirsimarja Blomqvist’s work on fast expert teams addresses the orchestration of trust and control in high-stakes problem-solving, bridging the need for controls with interpersonal and system-based trust cues. By synthesizing these scholarly emphases, the symposium advances a unified perspective on control-trust dynamics in the age of AI and Big Data, uncovering their interconnectedness and implications across societal, organizational, and digital ecosystems. AI, Control, Trust, and Political-Economic Power Author: Paul S. Adler; University of Southern California Control-Trust Dynamics in the Context of Agentic Algorithms Author: Markus Kreutzer; EBS University of Business and Law Author: Jorge Walter; The George Washington University Algorithmic Stakeholder Governance on YouTube: A Lead Role Perspective Author: Mareike Möhlmann; Bentley University Author: Robert Wayne Gregory; University of Miami Author: Ola Henfridsson; University of Miami Building trust and control for orchestrating temporary online knowledge collaboration Author: Kirsimarja Blomqvist; LUT University, LUT Business School

One of the most common way of analyzing the evolution of networks of interacting agents is based on simulation. The interactions among the agents can be modeled by N-person social dilemma games, and the evolution of the network can be characterized by repeated transitions. The nature of the transitions as well as the long-term behavior of the state of the network depend on the structures of the particular games under consideration. The purpose of this paper is to present the transitions that occur between N-person social dilemma games, the agent behavior causing these transitions, and the impacts of various parameters. Formerly, N-person social dilemma games such as Prisoners’ Dilemma, Chicken, Stag Hunt and Battle of the Sexes have been separately researched and analyzed. In this paper the specific behavior of Pavlovian agents are explored in a two dimensional cellular automaton environment using parameters that extend over all of these games. It is found that there are three significantly different types of agent behavior: bipartisan, partisan, and unison. Bipartisan agents stochastically decide to cooperate or defect based on a cooperating probability greater than zero and less than one, partisan agents either cooperate with certainty or defect with certainty, and unison agents cooperate 100% of the time. Each agent behavior can be associated with a set of social dilemma games and be represented as plateaus on a three dimensional graph. These plateaus themselves and the transitions between them are investigated in terms of where and why they occur. Lastly, this paper reviews the impact of initial cooperating probability, neighborhood size, learning factors, and grid size on these plateaus and transitions.

Experimental Economics: Economic and Game Theoretic Principles in Experimental Research in the Social Sciences

Extract Large and complex datasets collecting digital breadcrumbs of human behaviour have become non-negligible sources for social science researchers, students, and practitioners. ‘Big data’ has made a place for itself as a commodity in academic, commercial, and non-commercial settings. There have been numerous new descriptions for defining social scientists with quantitative expertise: empirical social scientist, computational social scientist, quantitative social scientists, social data scientist, etc. Nonetheless, a social scientist with solid big data skills is not yet a common profile in academia and research institutions. Neither is a computer scientist with broad interdisciplinary skills in social sciences. As the amount of published material increases exponentially in many disciplines, specialisation seems to be a reasonable response to keep up with the state of the art, and to produce technically and conceptually solid research. Interdisciplinary teams are vital in providing the critical synthesis from these separate specialisations, and this book is put together to help bridge the language gap between the different specialisations needed for migration analysis from big data sources.

Resident's Perceptions of the Role of Tourism in Social Changes of Rural Areas (Case Study: Ziarat Village of Gorgan County)

Forecasting: Volume 12 in: TIMS Studies in the Management Sciences, North-Holland, Amsterdam, 1979, vii + 373 pages, Dfl. 60.-, paperback

Mathematical models in systems biology can use quantitative techniques to study the integrated behaviors of biological systems in macro level. Agent-based modeling provides a qualitative framework, focusing on how each individual molecule behaves in micro level. We are motivated to combine their features together to describe the gene regulatory networks, in particular the emergence of macro phenomena from micro interactions. An agent-based model will be well defined as general as possible, including of many agents of various molecular species interacting in it. The mathematical premises, which are grounded in the micro behaviors of agents, can derive the chemical master equations and the stochastic differential equations for modeling the integrated behaviors of systems in macro level. Combing agent-based models with stochastic differential equations affords a more complete perspective on gent regulatory networks. In addition, the sources and magnitudes of deterministic dynamics and random noises are explicitly characterized.

Focus on Authors

Financial Technology (FinTech) refers to financial innovations which are technology-enabled including artificial intelligence (AI), big data analytics, blockchain and so on. As a disruptive revolution, the advancement of FinTech has changed the traditional methods of lending, payment and investment, and has emerged as a significant factor in the development of business and financing over the past decade. Furthermore, the adoption of FinTech such as digital bank not only promotes the prosperity of business at the macro level, but also changes daily behaviors of each tiny individual at the micro level. This paper aims to explore the innovations of financial technology in traditional financing channels from both macro and micro perspectives, to provide reasonable references for financial decision-makers. In conclusion, with the development of modern technology such as blockchain, big data, and APIs, FinTech is transforming the world on both a macro and micro level.

Businesses, government sectors, academia and society have been flooded with data which nowadays have become valuable economic assets. Data and Big Data Analytics (BDA) are vital for the succession of any organization. Big Data Analytics (BDA) delineates the approaches utilized to examine, process, analyze and expose hidden underlying patterns, interesting relations, and intelligence from huge datasets for different purposes. Despite the growth and widespread use of data analytics, the study on this research area is still at the nascent stage. The scenario is seen particularly for the developing countries like Malaysia. Previous literature has recognized data analytic development and its effect but has not yet provided a comprehensive review. This paper intends to conceptualize BDA manifestation, its application, and its challenges epistemology. This study employs a comprehensive review of past literature to serve the purpose. The findings of this paper will enhance the understanding of BDA conception epistemology (definition and application). This concept paper also intends to shed some light on BDA research areas by identifying the challenges surrounding BDA deployment. From the theoretical point of view, this paper contributes to providing BDA taxonomy based on the application in various industries. From the practical perspective, this paper contributes to BDA research field by identifying the obstacles that need to be managed by the BDA adopter for optimum utilization and decision making. From the government perspective, it contributes to Malaysian national agenda to becoming a leading regional BDA solution hub for all sectors and developed nation by the year 2025.

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of user-generated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA’s challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.