scholarly journals Jealousy in Cooperation — A Comparison of Two Game-Based Approaches

2020 ◽  
Vol 19 (02) ◽  
pp. 2050014
Author(s):  
F. Barachini ◽  
M. Bornemann

We are interested in the question of how jealousy influences the dynamics of populations. In a previous paper, we investigated the impact of power and jealousy on cooperation. By using intelligent agents, a stochastic updating process has been used, so that artificial agent populations could be modelled. In this paper, we investigate update procedures based on deterministic dynamics for populations arranged in a lattice. Compared to stochastic processes, our findings show that populations behave and react far more dynamically, in certain parameter regions, when spatial cooperation is applied.

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 528
Author(s):  
Lei Bao ◽  
Joseph Fritchman

Information definitions across many disciplines commonly treat information as a physical world entity. Information measures are used along with other physical variables undistinguished for modeling physical systems. Building on previous work, this research explicitly defines information as a unique category of entity that is created by intelligent agents to represent aspects of the physical world but that is not part of the physical world. This leads to the formation of the dual-space information modeling (DSIM) framework, which clearly distinguishes an information space from the physically based material space. The separation of information and material spaces allows new insight and flexibility into modeling complex systems. In this research, DSIM based agent models are applied to study the impact of information asymmetry to marketing behaviors. This paper demonstrates the effectiveness of the DSIM framework in the modeling process and how emergent behavior from these systems is encapsulated in the dual-space.


2008 ◽  
Vol 65 (4) ◽  
pp. 765-779 ◽  
Author(s):  
Dominique Pelletier ◽  
Joachim Claudet ◽  
Jocelyne Ferraris ◽  
Lisandro Benedetti-Cecchi ◽  
José Antonio Garcìa-Charton

Two kinds of approaches have been used for assessing conservation and fisheries-related effects of marine protected areas (MPAs): (i) statistical modelling based on field data and (ii) mathematical modelling quantifying the consequences of MPAs on the dynamics of populations, communities, and fisheries. Statistical models provide a diagnostic on the impact of MPAs on the ecosystem and resources; they are also needed for devising and assessing sampling designs for monitoring programs. Dynamic models enable exploration of the consequences of MPA designs and other management policies. We briefly review how each of these approaches has been implemented up to now in the literature and identify potential indicators of MPA effects that can be obtained from each approach to provide scientific advice for managers. Methodological gaps that impede the assessment of MPA effects and the construction of appropriate indicators are then discussed, and recent developments in this respect are presented. We finally propose ways to reconcile the two approaches based on their complementarity to derive suitable indicators to support decision making. In this respect, we suggest in addition that MPA managers should be associated from the beginning to the design and construction of indicators.


Author(s):  
Barin N. Nag ◽  
Dong-Qing Yao ◽  
Sungchul Hong

Agent-based auction trading is important in e-Procurement as a part of the supply chain management activity of procurement via the Internet. Participating buyers and sellers are intelligent agents tasked with finding matches with required or offered quantities for best performance. Formation of consortiums offers opportunities in matching trade volumes, but in the real world, there are difficulties in optimizing consortium formation due to lack of perfect information and the dynamic character of the information. Heuristic methods are often the only solution. This chapter shows the impact and capabilities of alternate heuristic models, and compares their performances in auction trading.


Mathematics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 103
Author(s):  
Evangelos Ioannidis ◽  
Nikos Varsakelis ◽  
Ioannis Antoniou

We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our network co-evolution model is actually a “learning mechanism”, where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size. We find that rational intelligent agents transform the network into a “centralized world”, reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge, as well as the “knowledge inequality”, grow exponentially.


2019 ◽  
Vol 11 (3) ◽  
pp. 29-37
Author(s):  
Mateusz Kot ◽  
Grzegorz Leszczyński

Abstract This study focuses on the development of a specific type of Intelligent Agents — Business Virtual Assistants (BVA). The paper aims to identify the scope of collaboration between users and providers in the process of agent development and to define the impact that user interpretations of a BVA agent have on this collaboration. This study conceptualises the collaboration between providers and users in the process of the BVA development. It uses the concept of the collaborative development of innovation and sensemaking. The empirical part presents preliminary exploratory in-depth interviews conducted with CEOs of BVA providers and analyses the use of the scheme offered by Miles and Hubermann (1994). The main results show the scope of the collaboration between BVA users and providers in the process of the BVA development. User engagement is crucial in the development of BVA agents since they are using machine learning algorithms. The user interpretation through sensemaking influences the process as their attitudes guide their behaviour. Apart from that, users have to adjust to this new kind of entity in the market and learn how to use it in line with savoir-vivre rules. This paper suggests the need to develop a new approach to the collaborative development of innovation when Artificial Intelligence is involved.


2021 ◽  
Author(s):  
Marija Mitrovic Dankulov ◽  
Bosiljka Tadic ◽  
Roderick Melnik

Abstract Predicting the evolution of the current epidemic depends significantly on understanding the nature of the underlying stochastic processes. To unravel the global features of these processes, we analyse the world data of SARS-CoV-2 infection events, scrutinising two eight-month periods associated with the epidemic’s outbreak and initial immunisation phase. Based on the correlation-network mapping, K-means clustering, and multifractal time series analysis, our results reveal universal patterns, suggesting potential predominant drivers of the pandemic. More precisely, the Laplacian eigenvectors localisation has revealed robust communities of different countries and regions that then cluster according to similar shapes of infection fluctuations. Apart from quantitative measures, the immunisation phase differs significantly from the epidemic outbreak by the countries and regions constituting each cluster. While the similarity grouping possesses some regional components, the appearance of large clusters spanning different geographic locations is persevering. Furthermore, cyclic trends are characteristic of the identified clusters, dominating large temporal fluctuations of infection evolution, which are prominent in the immunisation phase. Meanwhile, persistent fluctuations around the local trend occur in intervals smaller than 14 days. These results provide a basis for further research into the interplay between biological and social factors as the primary cause of infection cycles and a better understanding of the impact of socio-economical and environmental factors at different phases of the pandemic.


2019 ◽  
Vol 374 (1781) ◽  
pp. 20190008 ◽  
Author(s):  
Jakob Bro-Jørgensen ◽  
Daniel W. Franks ◽  
Kristine Meise

The impact of environmental change on the reproduction and survival of wildlife is often behaviourally mediated, placing behavioural ecology in a central position to quantify population- and community-level consequences of anthropogenic threats to biodiversity. This theme issue demonstrates how recent conceptual and methodological advances in the discipline are applied to inform conservation. The issue highlights how the focus in behavioural ecology on understanding variation in behaviour between individuals, rather than just measuring the population mean, is critical to explaining demographic stochasticity and thereby reducing fuzziness of population models. The contributions also show the importance of knowing the mechanisms by which behaviour is achieved, i.e. the role of learning, reasoning and instincts, in order to understand how behaviours change in human-modified environments, where their function is less likely to be adaptive. More recent work has thus abandoned the ‘adaptationist’ paradigm of early behavioural ecology and increasingly measures evolutionary processes directly by quantifying selection gradients and phenotypic plasticity. To support quantitative predictions at the population and community levels, a rich arsenal of modelling techniques has developed, and interdisciplinary approaches show promising prospects for predicting the effectiveness of alternative management options, with the social sciences, movement ecology and epidemiology particularly pertinent. The theme issue furthermore explores the relevance of behaviour for global threat assessment, and practical advice is given as to how behavioural ecologists can augment their conservation impact by carefully selecting and promoting their study systems, and increasing their engagement with local communities, natural resource managers and policy-makers. Its aim to uncover the nuts and bolts of how natural systems work positions behavioural ecology squarely in the heart of conservation biology, where its perspective offers an all-important complement to more descriptive ‘big-picture’ approaches to priority setting. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.


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