Examining social processes with agent-based models

This chapter highlights the importance of studying sustainability management and social responsibility issues from a systems perspective and argues that agent-based computational modelling is a suitable methodology for studying sustainability issues from an interdisciplinary, multilevel systems viewpoint. The chapter presents relevant computational-oriented works in the field of sustainability management, organized according to key research themes derived from the extant literature. The chapter also illustrates representative computational modelling works that focus on studying the adoption of corporate social responsibility practices. It argues that agent-based modelling can be used to capture not only the heterogeneous incentives of stakeholders, as more traditional methodologies have done, but also the intricate relations among such incentives that, additionally, may evolve over time. The chapter concludes by arguing that agent-based modelling can be fully leveraged if it takes into account considerations of intraorganizational dynamics, joint effects of first-order and second-order behavioural change, multilevel aggregation, systems resilience, interdependencies and temporal dynamics of stakeholders’ incentives, organizational structure, and complex forms of the firm–environment relations.

Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear-it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.

Wounds can arise either from a disease process or are surgically created as part of therapy. The healing of damaged tissue is a fundamental biological process, involving a complex set of cellular and molecular components acting within a specific spatial context. Impairment or aberration of the wound healing process is a considerable source of morbidity and mortality, likely only to increase in clinical significance given an aging population. Despite considerable advances in the mechanistic knowledge of wound healing, as with all complex biological processes converting that knowledge into effective therapeutics is a substantial translational challenge. As result, wound healing has been a major focus in the field of Translational Systems Biology, and, in particular, been a subject of agent-based mechanistic computational modeling. The intuitive mapping between biological knowledge and the rules in an agent-based model (ABM), the ability of an ABM to readily represent stochastic processes, and the inherent spatial representation capability of an ABM all facilitate the utilization of this method in the practice of Translational Systems Biology. Presented herein are a series of ABMs of wound healing that demonstrate the translational potential and utility of this methodology in advancing the rational development of wound healing therapeutics.

ABSTRACTAs common commensals residing on mucosal tissues, Lactobacillus species are known to promote health, while some Streptococcus species act to enhance the pathogenicity of other organisms in those environments. In this study we used a combination of in vitro imaging of live biofilms and computational modeling to explore biofilm interactions between Streptococcus oralis, an accessory pathogen in oral candidiasis, and Lactobacillus paracasei, an organism with known probiotic properties. A computational agent-based model was created where the two species interact only by competing for space, oxygen, and glucose. Quantification of bacterial growth in live biofilms indicated that S. oralis biomass and cell numbers were much lower than predicted by the model. Two subsequent models were then created to examine more complex interactions between these species, one where L. paracasei secretes a surfactant and another where L. paracasei secretes an inhibitor of S. oralis growth. We observed that the growth of S. oralis could be affected by both mechanisms. Further biofilm experiments support the hypothesis that L. paracasei may secrete an inhibitor of S. oralis growth, although they do not exclude that a surfactant could also be involved. This contribution shows how agent-based modeling and experiments can be used in synergy to address multiple-species biofilm interactions, with important roles in mucosal health and disease.IMPORTANCE We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms. Here, we used an iterative collaborative process between experimentation and modeling to reveal aspects of the mostly unexplored relationship between S. oralis and L. paracasei in biofilm growth. The inhibitory nature of L. paracasei on S. oralis in biofilms may be exploited as a means of preventing or alleviating mucosal fungal infections.

Including learning and forgetting processes in Agent-Based simulation models: Application to police intervention in out-of-hospital cardiac arrests

From the viewpoint of strong spatial cognition in graph problems, the shortest path can be identified in one physical action using strings and pins that respectively represent graph edges and vertices. By pulling a start and an end pin, we can construct a series of stretched strings as the shortest path. We use agent-based models (ABMs) to translate this action into computational representations. Assuming that a set of strings and pins are hung on a wall with a start pin, agents are disseminated downward to a destination as gravity forces. We implemented three models: a discrete-event, an asynchronous, and an aggregated agent dissemination on top of the MASS (multi-agent spatial simulation) library. To address large-scale network environments, we blended HDFS into MASS so that a graph data set is read over a cluster system in parallel. This paper presents these ABM implementations and performance measurements over a cluster system.

This paper introduces an agent-based computational economic modeling approach into an input–output analytical framework and proposes diffusion of technological innovation behavior into the simulation models. A large number of heterogeneous firms with macro-regional economic frameworks are included to perform policy simulation scenarios to investigate the impact of diffusing technological innovations on the dynamic changes in the regional economic structures of major global economies (i.e., China, Japan, the United States, Russia, India, and the European Union). This study reveals that process innovation may be more conducive to promoting the transfer of resource elements between regions for China, the EU, Japan, India, and Russia. However, for the U.S., product innovation may facilitate upgrading its industrial structure. Furthermore, from 2012 to 2030, for these six economies, the output share of the primary industry will likely decline by varying degrees, while the output share of the tertiary industry will show an uptrend. The employment share in the tertiary industry in these six economies decreased. Another important finding is that differentiated technological innovation-driven policies must be adopted within the context of global economic governance. Moreover, each economy should choose a technological innovation mode that is suitable for its economic development. Thus, these findings provide an important theoretical basis for formulating global economic governance policies in the future.

Originating from developments in computer science (also applied to natural sciences), a computational approach to the study of human behavior has developed that has gathered impetus in the literature during the 1990s. Agent-based computational models have become more and more used in the social sciences (i.e. in economics with the idea of Agent-based Computational Economics (ACE), and in sociology with the idea of Social Simulation). Different to the approach based on statistical analysis of behavioral data that aims to understand why specific rules are applied by humans, agent-based computational models presuppose (realistic) rules of behavior and try to challenge the validity of these rules by showing whether they can or cannot explain macroscopic regularities. In this introductory chapter, we argue that in order to study human populations, agent-based approaches are particularly useful from various theoretical perspectives. We urge demographers and other scholars interested in population studies to look at Agent-Based Computational Demography (ABCD) as a promising stream of research, which can improve our understanding of demographic behavior. We review and use the chapters of this book to substantiate our argumentations.

Heat baths and computational agent-based models

Heat Baths and Computational Agent-Based Modeling

From cell to multi-crypt: Agent-based models of the human colon suggests novel processes of Field cancerisation

La sociologia analítica és una aproximació a la teorització social «d’abast mitjà» que intenta desenvolupar explicacions precises, realistes i basades en l’acció sobre diversos fenòmens socials. En el nucli de l’enfocament analític es troba la idea que les explicacions acceptables han d’explicar els mecanismes a través dels quals es van produir els fenòmens que es volen explicar. En aquest article discutim els elements més importants d’aquest enfocament i intentem il·lustrar els diversos components que integren l’explicació del canvi social. L’enfocament analític adoptat busca integrar d’una manera estreta les teories de l’acció i de la interacció basades en mecanismes amb els models computacionals basats en agents. L’article s’organitza de la manera següent: 1. Presentem les idees guia que hi ha al darrere de l’enfocament dels mecanismes. 2. Discutim diversos mecanismes d’acció i interacció amb l’objectiu d’il·lustrar com funciona l’enfocament. 3. Utilitzem un model basat en l’agència per il·lustrar com poden vincular-se els mecanismes de nivell individual amb els resultats socials. 4. Mostrem la importància de calibrar empíricament aquest tipus de models. 5. Acabem amb unes observacions finals.

In Silico Translation of Cellular and Molecular Mechanisms to Clinical Phenomena in Atheroma Development with an Agent Based Model

Chapter 6 - Agent-Based Modeling and High Performance Computing

In this chapter it is argued that when the Austrian revival takes place in the 1970s and 1980s the image of economics as an analytical science which can be methodologically kept clean from value judgments, and the economist as a pure truth-seeker shapes modern Austrian economics at the expense of an idea of a socially involved, embedded scholar with a responsibility toward society which was characteristic of the pre-WWII Austrian school. The neglect of that part of the Austrian heritage is important not only for how we understand the role and responsibility of the social scientist but also because it alters what we consider to be relevant and valid economic knowledge. The chapter demonstrates that insight into economic processes was excluded from what was considered valid economic knowledge and how social relevance of knowledge was no longer a goal in the postwar Austrian School. The chapter identifies alternative currents in the modern Austrian school to this general trend and suggests ways forward to think about the appropriate institutions to promote relevance and the moral conduct of (Austrian) economics.