Collective Behavior Under the Umbrella of Blockchain

2020 ◽  
pp. 425-445
Author(s):  
Kanak Saxena ◽  
Umesh Banodha
Keyword(s):  
Social Psychology ◽  
Artificial Intelligence ◽  
Collective Behavior ◽  
New Technologies ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Digital World ◽  
Current Scenario ◽  
Future Potential ◽  
Or Organization

Any social or organization system will fetch the properties from economics, sociology, and social psychology. In the digital world everyone is trying to cope with the new technologies for the survival. The dynamics of such a system are very multifarious due to the complexity in the convergence of the digital, physical, and biological realms. The dynamics of the society and organization are rapidly changing due to the imparting of the new technologies, such as artificial intelligence, internet of things, virtual reality, etc. The resultant is revolutionizing of opportunities and expectations due to the changes in the values, norms, identities, and future potential. The collective behavior (CB) plays an important role in predicting the various dynamics which are not only coherent but also paying attention. Blockchain will not only help in detecting but also help in finding the major causes and challenges for current scenario dynamics. The chapter describes the agent-based modeling and ant colony optimization components of the CB.

Human Factor Role for Cyber Threats Resilience

2019 ◽  
pp. 215-241
Author(s):  
Zlatogor Borisov Minchev
Keyword(s):  
Human Factor ◽  
New Technologies ◽  
Mixed Reality ◽  
Smart Environments ◽  
Methodological Framework ◽  
Agent Based ◽  
Cyber Threats ◽  
Digital World ◽  
Technological Growth ◽  
Scenario Method

The chapter describes the problem of building cyber threats resilience for the human factor as the technological growth is constantly changing the security landscape of the new digital world. A methodological framework for meeting the problem by using the “scenario method” and experts' support is outlined. An implementation of comprehensive morphological and system analyses of cyber threats are performed, followed by agent based mixed reality validation, incorporating biometrics monitoring. The obtained results demonstrate a correlation of experts' beliefs for cyber threats identification, related to human factor biometric response, whilst using social networks and inhabiting smart environments of living. The achieved results prove “use with care” necessity for new technologies, concerning cyber threats landscape for assuring a sustainable resilience balance from the human factor perspective.

scholarly journals Human Nature in the Adaptation of Trust

2007 ◽  
pp. 123-140 ◽  
Author(s):  
B. Nooteboom
Keyword(s):  
Social Psychology ◽  
Human Nature ◽  
Agent Based Modeling ◽  
Central Question ◽  
Agent Based Model ◽  
Agent Based ◽  
Frame Switching ◽  
Behavioral Phenomena

This chapter pleads for more inspiration from human nature in agent-based modeling. As an illustration of an effort in that direction, it summarizes and discusses an agent-based model of the build-up and adaptation of trust between multiple producers and suppliers. The central question is whether, and under what conditions, trust and loyalty are viable in markets. While the model incorporates some well-known behavioral phenomena from the trust literature, more extended modeling of human nature is called for. The chapter explores a line of further research on the basis of notions of mental framing and frame switching on the basis of relational signaling, derived from social psychology.

Human Factor Role for Cyber Threats Resilience

2016 ◽  
pp. 377-402
Author(s):  
Zlatogor Borisov Minchev
Keyword(s):  
Human Factor ◽  
New Technologies ◽  
Mixed Reality ◽  
Smart Environments ◽  
Methodological Framework ◽  
Agent Based ◽  
Cyber Threats ◽  
Digital World ◽  
Technological Growth ◽  
Scenario Method

The chapter describes the problem of building cyber threats resilience for the human factor as the technological growth is constantly changing the security landscape of the new digital world. A methodological framework for meeting the problem by using the “scenario method” and experts' support is outlined. An implementation of comprehensive morphological and system analyses of cyber threats are performed, followed by agent based mixed reality validation, incorporating biometrics monitoring. The obtained results demonstrate a correlation of experts' beliefs for cyber threats identification, related to human factor biometric response, whilst using social networks and inhabiting smart environments of living. The achieved results prove “use with care” necessity for new technologies, concerning cyber threats landscape for assuring a sustainable resilience balance from the human factor perspective.

Modeling the FX Market Traders’ Behavior

2013 ◽  
pp. 303-338 ◽  
Author(s):  
Monira Aloud ◽  
Edward Tsang ◽  
Richard Olsen
Keyword(s):  
Collective Behavior ◽  
Agent Based Modeling ◽  
Statistical Properties ◽  
Trading Behavior ◽  
Stylized Facts ◽  
Collective Behaviors ◽  
Agent Based ◽  
Trading Agents ◽  
Individual Traders ◽  
Fx Market

In this chapter, the authors use an Agent-Based Modeling (ABM) approach to model trading behavior in the Foreign Exchange (FX) market. They establish statistical properties (stylized facts) of the traders’ trading behavior in the FX market using a high-frequency dataset of anonymised OANDA individual traders’ historical transactions on an account level spanning 2.25 years. Using the identified stylized facts of real FX market traders’ behavior, the authors evaluate the collective behavior of the trading agents in resembling the collective behavior of the FX market traders. The study identifies the conditions under which the stylized facts of trading agents’ collective behaviors resemble those for the real FX market traders’ collective behavior. The authors perform an exploration of the market’s features in order to identify the conditions under which the stylized facts emerge.

scholarly journals AGENT-BASED SOCIAL PSYCHOLOGY: FROM NEUROCOGNITIVE PROCESSES TO SOCIAL DATA

2011 ◽  
Vol 14 (05) ◽  
pp. 711-731 ◽  
Author(s):  
NESTOR CATICHA ◽  
RENATO VICENTE
Keyword(s):  
Social Psychology ◽  
Collective Behavior ◽  
Information Exchange ◽  
Imaging Techniques ◽  
Cognitive Strategies ◽  
Political Affiliation ◽  
Moral Foundations ◽  
Moral Foundation ◽  
Agent Based ◽  
Agent Model

Moral Foundation Theory states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations. The use of functional imaging techniques has revealed a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information that is correlated to political affiliation. Here we characterize the collective behavior of an agent-based model whose inter individual interactions due to information exchange in the form of opinions are in qualitative agreement with experimental neuroscience data. The main conclusion derived connects the existence of diversity in the cognitive strategies and statistics of the sets of moral foundations and suggests that this connection arises from interactions between agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data on conformity and learning processes, presents statistical signatures consistent with moral judgment patterns of conservatives and liberals as obtained by survey studies of social psychology.

scholarly journals A research paper of Hossein Sabzian (2019), Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners, ArXiv, 54p

2020 ◽  
Author(s):  
Dai-Long Ngo-Hoang
Keyword(s):  
Differential Equations ◽  
Network Effects ◽  
New Technologies ◽  
Complex Adaptive System ◽  
Agent Based Modeling ◽  
Housing Bubble ◽  
Agent Based ◽  
Economic Agents ◽  
Macro Scale ◽  
Housing Bubbles

Nowadays, we are surrounded by a large number of complex phenomena such as virus epidemic, rumor spreading, social norms formation, emergence of new technologies, rise of new economic trends and disruption of traditional businesses. To deal with such phenomena, social scientists often apply reductionism approach where they reduce such phenomena to some lower-lever variables and model the relationships among them through a scheme of equations (e.g. Partial differential equations and ordinary differential equations). This reductionism approach which is often called equation based modeling (EBM) has some fundamental weaknesses in dealing with real world complex systems, for example in modeling how a housing bubble arises from a housing market, the whole market is reduced into some factors (i.e. economic agents) with unbounded rationality and often perfect information, and the model built from the relationships among such factors is used to explain the housing bubble while adaptability and the evolutionary nature of all engaged economic agents along with network effects go unaddressed. In tackling deficiencies of reductionism approach, in the past two decades, the Complex Adaptive System (CAS) framework has been found very influential. In contrast to reductionism approach, under this framework, the socio-economic phenomena such as housing bubbles are studied in an organic manner where the economic agents are supposed to be both boundedly rational and adaptive. According to CAS framework, the socio-economic aggregates such as housing bubbles emerge out of the ways agents of a socio-economic system interact and decide. As the most powerful methodology of CAS modeling, Agent-based modeling (ABM) has gained a growing application among academicians and practitioners. ABMs show how simple behavioral rules of agents and local interactions among them at micro-scale can generate surprisingly complex patterns at macro-scale. Despite a growing number of ABM publications, those researchers unfamiliar with this methodology have to study a number of works to understand (1) the why and what of ABMs and (2) the ways they are rigorously developed. Therefore, the major focus of this paper is to help social sciences researchers get a big picture of ABMs and know how to develop them both systematically and rigorously.

A Survey on Explainability in Artificial Intelligence

2022 ◽  
pp. 55-75
Author(s):  
Prarthana Dutta ◽  
Naresh Babu Muppalaneni ◽  
Ripon Patgiri
Keyword(s):  
Artificial Intelligence ◽  
Broad Spectrum ◽  
New Technologies ◽  
Research Community ◽  
Current Situation ◽  
Learning Technologies ◽  
The World ◽  
Current Scenario ◽  
Modern Technologies

The world has been evolving with new technologies and advances everyday. With learning technologies, the research community can provide solutions in every aspect of life. However, it is found to lag behind the ability to explain its prediction. The current situation is such that these modern technologies can predict and decide upon various cases more accurately and speedily than a human, but has failed to provide an answer when the question of “how” it arrived at such a prediction or “why” one must trust its prediction, is put forward. To attain a deeper understanding of this rising trend, the authors surveyed a very recent and talked-about novel contribution, “explainability,” which would provide rich insight on a prediction being made by a model. The central premise of this chapter is to provide an overview of studies explored in the domain and obtain an idea of the current scenario along with the advancements achieved to date in this field. This survey aims to provide a comprehensive background of the broad spectrum of “explainability.”

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