Ebook: Complex Societal Dynamics

If we conceptualise society as a complex system, we may observe that its complexity arises from a multitude of its constituent elements – individuals, groups, organisational units, etc. - and from a multitude of their interconnections. Moreover, these interrelations are themselves complex in a sense that the “state” of each element depends not only on its previous state, but also on a complex combination of previous states of many other elements. Yet more, humans build their own perceptions of various dependencies between a social system’s elements, which affect further development of these dependencies, which, in turn, may influence the very perceptions. We argue that this complex-systems-view-of-society may help us better understand the often-cited “ambiguity” of the notion of security, as well as the shifts of foci of security concerns, such as the one that marked the ending of the Cold War. While, during the Cold War period, the focus of security concerns was at the national or the state level, after the end of the Cold War, the focus moved to the sub-state, the group and the individual level, as well as to the supra-state and the global level. What had previously been regarded as “monolithic” national security, now became a more diffuse and ambiguous concept with multiple meanings. The task of an analyst in those new circumstances became much harder. Instead of “monolithic” states with mutually conflicting, but structurally similar national interests, analysts of the post-Cold-War world have been confronted with much greater heterogeneity of identities and interests of multitudes of individual and group actors. In contrast with a few relatively simple inter-dependencies between the Cold War states, based mostly on mutual balance of military power, multiple interindividual and intergroup ties have spanned multiple dimensions, being subject to incessant commotion and change. In contrast to passive, inanimate states, the “new security environment” abounds with perceptive human subjects, capable of influencing current and constructing ever new, yet unanticipated realities. The new security environment requires new, more adequate methods of studying and analysis. We argue for the methods of complex systems research to provide these much needed new tools. The arguments that we provide are general, pointing to the more concrete examples of application of complex systems methods to particular security challenges and opportunities, which are further discussed as part of the remaining contributions to this workshop.

Evidence-based approaches to international policy-making may improve the quality of policy decisions compared to traditional decision making principles. For example, decision makers traditionally rely on existing administrative boundaries when establishing new international borders following the dissolution of a state or the secession of part of that state. An evidence-based alternative could involve consideration of numerous other factors and goals in establishing a border. Policy informatics, encompassing new knowledge, tools, and approaches should become increasingly useful for informing policy decisions, especially where decision makers are faced with a constant influx of information, conflicting values and political pressures. Specifically, a complexity approach has shown utility in effectively extracting useful patterns from complex landscapes in various science areas. However, its applications to public policy problems have been limited due to some unique obstacles and barriers inherent to these problems. Through the example of border resolutions, we examine the evolution of using scientific reasoning to make policy choices and how we might be able to include a complexity approach more regularly in evidence-based policy debates.

Terrorism as a political and security phenomenon has always attracted, in various forms, the interest of the international community, and affected security establishments in different countries and regions all over the world. Although we can trace its historical roots 200 years back, the most significant impact on safety was in the 20^th and at the beginning of the 21^st century. From state-sponsored terrorism (which will be remembered in history as an attempt to satisfy the legitimacy of terror and protection of foreign interests), through internationalization of national liberation wars and ethnic conflicts, to the left and right-oriented terrorist groups in Europe, terrorism was always determined by two basic appearances: terror and violence. These appearances forced European countries, unsatisfied with global efforts to combat terrorism in the last century, to find common solutions in creating an institutionalized framework for cooperation.

A complex social system consists of a variety of social elements and processes. One of the crucial problems which social researchers try to understand and explain is how social order and collective phenomena emerge from individual behaviours, and what the specific features of a particular type of social aggregate are. Another important problem is the application of appropriate research methods that allow explication of the distinctiveness of social reality. Recently there has been increased interest in the use of agent-based modelling, social network analysis, and socio-physics. These novel approaches often treat social phenomena as any other physical phenomena. We believe that these methods should take into account peculiarities of social realm in greater extent. We would like to discuss some of these novel approaches with placing special attention on some methodological problems that result from usage of models for investigation of complex social behaviours. In this paper we discuss the problem of macroscopic effects of microscopic forces using social constructions of security and threat as an example.

Human social systems are the most complex systems that we can conceive of, as interaction among their members is by messages which have to be interpreted before they can take any effect — and the interpretation of identical messages sent and received in identical situations can have divergent effects as human actors have long-term memories which makes the effects highly path-dependent. The emergent phenomena in human social systems are therefore even more difficult to understand than related phenomena in anthills or chemical systems. The paper introduces models of immergence and second-order emergence to explain the innovation of norms in human groups of moderate size — as opposed to the the emergent phenomena in human crowds which are quite similar to the phenomena that can be observed in physical and animal systems. Modelling the innovation of norms and norm systems makes it necessary that interactions between model agents can be manifold and path-dependent, i.e. that agents have a long memory and deliberation capabilities. This is shown with an example from modelling criminal or terrorist behaviour.

Our civilization is a complex system of individuals interacting with the environment. Societal dynamics develops via human actions, which are governed by decisions. The action of individuals is always a choice among the possibilities recognized as allowed by the circumstances. The choice always implies a decision. The theory of human decisions will be discussed with an emphasis on the difference between simple system and complex system approaches. The basic criterion for decisions is that the best is selected, but which is the best one? Founders and best proponents of utilitarianism, Jeremy Bentham (1748-1832) and John Stuart Mill (1806-1873) started with the postulate known as the “greatest happiness principle”. It can be viewed as the governing law of human actions. In the first part we summarize the way in which the greatest happiness principle did appear in the history. In the second part we outline a new approach to axiomatic economics, with the greatest happiness principle as the basic axiom. The result is a new dynamic microeconomics where the maximization rule of rational-choice decisions is replaced by a force law. The resulting theory is consistent with both physics and economics, especially the resource and environmental branches.

In this paper we investigate the ethical implications of the Greatest Happiness Principle (GHP) in a complex system approach. The main question in political philosophy is: What do we need to do in order to live together well? In the complex approach, based on the wealth increase law, we take into account (i) the parameters, which may change by human decisions, as well as (ii) the long-term expectations, which motivate the decisions themselves. Factors (i) are material goods, money, parameters of human physiology (e.g. health), psychology (knowledge) and sociology (e.g. friends, power). These quantities are measurable in principle, i.e. they can be mapped onto the set of real numbers. The changes refer to exchanges between two agents or with nature, and there is production/consumption internal to agents. It is a hot topic whether there is a need for culture politics, or the market forces should govern culture too. Our results show that culture, knowledge and social relations follow different ethics from the one governing the exchange of the material goods, and there is no real market force at work for cultural resources. In particular, the Greatest Happiness Principle for the society provides rules and ethical demands for such transfers.

This paper incorporates interdisciplinary New Institutional Economics, and suggests a framework for analyzing and improvement of governance of socio-economic dynamics of agriculture. It takes into account: the role of specific institutional environment (formal and informal rules and rights, and systems of enforcement; behavioral characteristics of agents (preferences, bounded rationality, tendency for opportunism, risk aversion); costs of governance and critical factors of transactions (uncertainty, frequency, assets specificity, appropriability); comparative efficiency of market, private, public and hybrid modes; efficiency of alternative modes for public intervention; complementarities between modes; needs for multilateral and multilevel governance; technological and eco-factors.

Anthropology, economics, sociology, political science and the crossover social sciences base their research on some simpler or more complex model of the individual and the society. These models, depending on their complexity, focus on matching real-world behaviors in one aspect or another, and are usually designed for a specific purpose. This, however, can cause problems when we try to create a general model. The problem is most noticeable in case of economics: “micro” models describe the individual, or maybe smaller decision making-units (families, extended families, etc.), but these fail to integrate into a larger model of society. Conversely, “macro” models depict the behavior of states or countries, yet usually are unrealistic in their representation of the individual. The paper suggests that by using agent-based computer simulation the distance between micro and macro approaches can be significantly reduced. Instead of using an elaborate symbolic modeling scheme, agent-based simulation will be introduced that on the micro level will contain independent agents whose behavior mimics that of real individuals, whereas the emergent system will match that of observed macro-behavior. To demonstrate the workings of this approach, a series of case studies will be explored that will guide the reader through the construction of a simulated world, research practices in these simulations, and the possible far-reaching results the approach can bring.

Social conflict entails a variety of social phenomena, including international conflict, civil war, genocide, organized violence, insurgencies and rebellions, terrorism, riots, etc. Given the heterogeneity of social phenomena encompassed by this notion, it is not surprising that a variety of methodological and theoretical approaches have been applied to study it, ranging from formal game theoretic models to the hermeneutics of narratives. Social conflict has also been studied by means of complex systems research methods, such as agent-based social simulation. We conduct a review of the main formal-theoretical approaches to social conflict including agent-based modeling. We promote the usage of agent-based social simulation for it affords shedding light onto the nature of generative processes related to social conflict. We discuss the implications of such an approach to the study of social conflict against orthodox research designs and point toward its advantages which may facilitate development of more adequate conflict prevention and conflict management procedures.

Over the next decades, natural resources and water resources in particular are likely to be one of the major origins of social conflicts. To date however, no model enables the study of coupled dynamics of hydrology, water use and social conflicts. Building such a model requires identifying the key concepts, entities and processes that are present in much field cases and have to be included into the model. The research presented in this paper takes place in a more general project named MAELIA for Multi-agent for Environmental Norms Impact Assessment. The purpose of MAELIA is to provide a decision-support model helping decision makers and stakeholders to manage water resources. This model aims to be generic enough for it to be applied in various field cases at different scales. We describe the actors involved in water management or water use using an agent-based approach. Water monitoring institutions and water users are described as agents in interaction within a stylised representation of a watershed basin. We propose a conceptual model that describes not only the hydrology in the basin and the water consumption behaviour of users, but also the representation of both the users at the institutional level and the power relationships that determine the arbitration of norms about water use. We propose two possible uses of this model. The first is the analysis of the impacts of several norms for detecting potential conflicts. The second possible use explores the local formulation of norms given the balance of powers in already settled social conflicts. This generic platform modelling conflicts on natural resources may thereby provide new insights into the analysis of well-known natural resource related conflicts, such as the Gauvery dispute in India.

The paper explores methodological question of how to evaluate the evidence of simulation models of complex systems. This is the process of model validation. Since simulation models provide a tool for investigating social mechanisms, model validation requires an approach that differs from the analysis of statistical significance of variables. The conceptual difference can be explained by distinguishing clear and distinct terms, a terminology of early modern philosophy. It is suggested that model validation benefits from making use of Max Weber’s concept of adequate causality. This can be investigated by means of sensitivity analysis. A mechanism is causally adequate if the model is not robust under variation. This is illustrated by an example. An analysis of this example reveals the significance of extreme events. In complex systems, extreme events provide evidence of the presence of presumed social mechanisms that are implemented in a model. This implies regarding conflicts as extreme events of societal operations that indicate fundamental social mechanisms.

While most efforts to curb terrorism concentrated on neutralizing terrorist nets, very little is known about the social space which is open to terrorist moves. Introducing the notion of social permeability to terrorism, we study the role of passive supporters to a terrorist cause using the physical theory of percolation. A passive supporter is a normal citizen who identifies with the terrorist cause but without any direct involvement with either the terrorist group or its activities. It is an individual dormant attitude associated with a personal opinion which characterizes passive support. It does not need to be explicitly claimed. Passive supporters just do not oppose a terrorist act in case they could. Unnoticeable, most of them reject the violent aspects of terrorist acts. They only partly share the terrorist cause. The question of the range of a terrorism threat is then analyzed in terms of a percolation phenomenon within a multi-dimensional social space. Traditional terrorism is found to correspond to non-percolating situations while international terrorism is associated to worldwide percolation. Using only military means to eradicate terrorism is shown to be inefficient. Hints are given on how to diminish the terrorism threat without military destruction.

We discuss some social contagion processes to describe the formation and spread of radical opinions. We use threshold dynamics to describe the local spread of opinions, and mean field effects. We calculate and observe phase transitions in dynamical variables resulting in a rapidly increasing number of passive supporters. Results strongly indicate that military solutions to the problem of terrorism are inappropriate.

The notions of complex systems and emergence are rather closely related. Nevertheless, both are prevalently identified after a dynamics of the system is known and sufficiently understood. This article focuses on the possibility for drawing conclusions on one of these notions if some data about the other are known. On the one hand, we analyse the possibility of determining the likelihood of emergent phenomena occurrence during the evolution of a complex system dynamics. On the other hand, we analyse the possibility of predicting dynamics of a complex system if one knows some characteristics of emergent phenomena in it. The basis of the approach consists in attributing analogues of quantities from physics to a model of a complex system. The emphasis is on relating a complex system’s evolution and localised-in-time emergent phenomena.

This paper begins by contextualizing human interaction networks as both arbiters and products of specific cultural practices. The various social scientists’ attempts to organize these network relationships into specific models, while notable for attempting to provide a usable framework for analyzing human interaction, share weaknesses that reduce these complex systems into simple and ultimately unreliable predictors of behavior. The analysis is expanded to include virtual world designers whose development of fictional cultures and societies ultimately provides a more useful simulation through which to examine human interaction. What makes these virtual environments a more successful model than others? What lessons can be drawn from their development cycles? How can we utilize these models for the benefit of all researchers involved with complex societal dynamics? Answers to these, and other questions, are combined with a discussion of anthropological research methodology – specifically participant observation and ethnographic fieldwork. This paper suggests that the data gathered from one branch of the social sciences can be more effectively utilized to provide more accurate information for modeling complex systems. A case study of one such enhanced model is discussed as well as recommendations for future directions and areas of interest for the study of human interaction networks.

This paper is reporting some illustrative research results concerning complex societal systems obtained by our Web Mining Group at the Department of Ergonomics and Psychology of the Budapest University of Technology and Economics (BME), also supported by the Information Management Department of the University of West Hungary (NYME). Our starting point is the remark of John D Sterman [1]: “Change is accelerating, and the complexity of the systems in which we live is growing. Increasingly change is the result of humanity itself. As complexity grows so do the unanticipated side effects of human action, further increasing complexity in a vicious cycle. Many scholars call for the development of ‘systems thinking’ to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world, and using the new information we revise the decisions we make and the mental models that motivate those decisions.” Our main – both practical and theoretical – question is “how do people learn in and about complex dynamic systems?” Therefore we investigate the interaction between a human and his/her Virtual Learning Environment (VLE) with the assistance of up-to-date sophisticated Web data mining tools – the SPSS MODELER and the SPSS TEXT ANALYTICS. Since Web mining can be described as an application of data mining under special circumstances, a definition of data mining is required first: “Data mining is also called Knowledge Discovery in Databases (KDD). It is commonly defined as the process of discovering useful patterns or knowledge from data sources, e.g. databases, texts, the Web, etc. The patterns must be valid, potentially useful and understandable. Data mining is a multi-disciplinary field involving machine learning, statistics, databases, artificial intelligence, information retrieval, and visualization” [2, p. 6]. A similar general definition applies to Web mining: “Web mining aims to discover useful information or knowledge from Web hyperlink structure, page content, and usage data” [2, p. 6]. According to this definition, different types of Web mining can be identified as: Web structure mining, Web content mining, and Web usage mining. Our examinations have exclusively dealt with Web usage mining so far. As far as the project’s Web mining aspects are concerned, we relied on the notions and procedure systems of the already mentioned excellent book [2]. In this paper Web usage mining is considered as a supportive tool in investigating complex societal systems.

The theory of complex dynamic ecosystems views economy of the state as complex configurations of multitudes of economic actors (individuals, firms, industries, states) engaged in overlapping and interlocking patterns of relationship with one another and with environment. Ecological-economic systems are groups of interacting, interdependent parts linked together by exchanges of energy, matter, capital and information. Economic dynamics is complex in the sense that it is characterized by heterogeneity of economic actors’ capabilities, desires, needs and knowledge, high interrelatedness among economic actors, emergence of macro-level properties from multiple individual interactions, and the rise of feedback effects caused by economic actors’ capabilities of recognizing and responding to the emergent macro-level features. This is a report on using systems thinking and system dynamics methodology to understand complex, dynamic ecological-economic and managerial problems. One objective is to learn system dynamics, a simulation based methodology to analyze complex dynamic ecological-economic problems. To this purpose, concepts of dynamic complexity, tools of dynamic feedback modeling, the important role of feedback, delays and nonlinearities, and basic dynamic feedback structures are covered.

The appearance of a new threat to humanity – nanothreat – is connected to the epoch of discoveries in the field of physics and chemistry at the end of the last century: 1) By the creation of electronic microscopes capable to identify individual molecules and nanodimensional objects such as nanoparticles (nanophases) and nanostructures; 2) By detection of a new state of substance, the properties of which radically differ from all previously known. These substances are extremely chemically active and their influence on living organisms is largely not known yet. Nanoparticles from the environment, food and clothes can easily penetrate human organism by all accessible routes (nose, mouth and skin) and weaken (or injure) various organs. Therefore the nanoecological threat is practically uncontrollable. 3) By the appearance of an opportunity to manipulate separate molecules, by atoms and nanoparticles, and to create nanorobots, various optical and electronic nanosystems (which can be capable of self-reproduction). However, the process of self-reproduction of “smart” nanosystems potentially resulting with “grey goo” can be uncontrollable. Nanotechnological threat is connected to the creation of nanoweapons as well as nanosystems for the replacement of natural organs, potentially transforming Homo sapiens into nanopeople. Nanoprogress is created (as a rule) simultaneously with the development and deepening of democracy, therefore personal nanosensors of residence, intelligence and feelings will present the main threats at the nanodemocracy epoch.

Problems of optimal control of economic and social systems can be divided in two levels. The first, internal level is to optimize behavior of agents in terms of their objective functions. The second, external level is to choose the objective functions of the agents. It is necessary to reach certain aims of the system. The resulting objective functions can be found in a class of indexing functions.