Agent-based modeling

Purpose—The paper uses part of the distributed cognition literature to study how employees cope with organizational plasticity, in an attempt to identify the characteristics of cognitive plasticity.
Design/methodology/approach—Evidence is collected by designing and implementing an agent-based computational simulation model (the IOP 2.0) where employees have the option to use external resources and the social environment to perform tasks. Since plasticity is more effective when change and uncertainty are high, the simulation features an increase in the difficulty and number of tasks to which employees need to cope.
Findings—Cooperation and sharing of competence and ability are key to cognitive plasticity. Being able to master the use of some resources, together with other employees competencies make some achieve the most efficient task performance.
Practical implications—Findings suggest that, under conditions of change and plasticity, HRM shall attempt to develop measures to support employees' cognitive skills necessary to cope with it, for example, mostly through diagnosis, training, and facilitating on-the-job dialogue.
Originality/value—This is the first study that attempts a merger between organizational cognition and plasticity, and it is the first to match its results to HRM policy recommendations.

The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.

Agent-based social simulations are typically described in imperative form. While this facilitates implementation as computer programs, it makes implicit the different assumptions made, both about the functional form and the causal ordering involved. As a solution to the problem, a probabilistic graphical model, Action Network (AN), is proposed in this paper for social simulation. Simulation variables are represented by nodes, and causal links by edges. An Action Table is associated with each node, describing incremental probabilistic actions to be performed in response to fuzzy parental states. AN offers a graphical causal model that captures the dynamics of a social process. The details of the formalism are presented, along with illustrative examples. A software implementing the formalism is available in http://actionnetwork.epizy.com.

This poster describes a simulation investigating the interaction and effectiveness of individual and social learning in producing workable budgeting strategies. It is motivated by an attempt to understand interview data about money management practices collected from a sample of recently retired households. Social learning proves to be very important when agents are boundedly rational and also leads to the emergence of differentiation in simulated 'lifestyles'.

Archaeological remains around the world are testament that large-scale construction projects have been successfully carried out for millennia. This success is particularly evident through the great infrastructural works of the Roman Empire. Yet, it was when the capital was moved from Rome to Constantinople that the largest of these projects was undertaken. This megaproject of the fourth-and fifth-century water supply was made of hundreds of kilometres aqueduct channels and bridges that brought fresh water to the city's complex system of reservoirs and cisterns. Unlike projects of the previous centuries, we are left with no written record of how this titanic project was undertaken and existing archaeological and historical commentaries on structures of this period do not provide details of organization of construction. We explore the nature of building Constantinople's water supply through diverse sources of knowledge and the application of agent-based modelling – a method for simulating the actions, interactions and behaviours of autonomous agents and the resulting emergent properties of the system in which they are a part. This paper demonstrates the ability of ABM to develop and test richer hypotheses about historical construction organization and management than physical and historical evidence on their own.

Social relationships such as friendship and partner choice are ruled by the proximity principle, which states that the more similar two individuals are, the more likely they will become friends. However, proximity, similarity, and friendship are concepts with blurred edges and imprecise grades of membership. This study shows how to simulate these friendship dynamics in an agent-based model that applies fuzzy sets theory to implement agent attributes, rules, and social relationships, explaining the process in detail. Although in principle it may be thought that the use of fuzzy sets theory makes agent-based modelling more elaborated, in practice it saves the modeller from taking some arbitrary decisions on how to use crisp values for representing properties that are inherently fuzzy. The consequences of applying fuzzy sets and operations to define a fuzzy friendship relationship are compared with a simpler implementation, with crisp values. By integrating agent computational models and fuzzy set theory, this paper provides useful insights into scholars and practitioners to tackle the uncertainty inherent to social relationships in a systematic way.

This paper proposes a computer simulation framework for investigating the role of built form – specifically physical venues-in the dynamics of neighborhood integration and segregation. Looking to Schelling's classic work (1971) and the subsequent literature, we note, however, that with few exceptions 'form' in these models is reduced to an agent's capacity to 'see' nearby grid-cells of the simulation world: an agent's neighbourhood is its Moore neighbourhood. In this research, we argue that an analytically meaningful simulation of neighbourhood formation – or more specifically of integration and segregation dynamics – must acknowledge the role of built form. To do so, we introduce physical venues into the classic Schelling model in order to reconsider the simulation's dynamics as influenced by both the spaces where agents live and the spaces of their activities. Our analysis proceeds through a series of four case studies of increasing sophistication, considering the impact of different spatial configurations as well as different kinds of venue. We articulate a simple but powerful generative model of urban social formations that also may offer insight into the conditions under which sedimented patterns of segregation may become unsettled.

We built a multiagent simulation of urban traffic to model both ordinary traffic and emergency or crisis mode traffic.
This simulation first builds a modeled road network based on detailed geographical information. On this network, the simulation creates two populations of agents: the Transporters and the Mobiles. Transporters embody the roads themselves; they are utilitarian and meant to handle the low level realism of the simulation. Mobile agents embody the vehicles that circulate on the network. They have one or several destinations they try to reach using initially their beliefs of the structure of the network (length of the edges, speed limits, number of lanes etc.). Nonetheless, when confronted to a dynamic, emergent prone environment (other vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather reactive agent will activate more cognitive modules to adapt its beliefs, desires and intentions. It may change its destination(s), change the tactics used to reach the destination (favoring less used roads, following other agents, using general headings), etc.
We describe our current validation of our model and the next planned improvements, both in validation and in functionalities.

Institutional frameworks powerfully determine the goals, violence, and trajectories of identitarian movements—including secessionist movements. However, both small-N and large-N researchers disagree on the question of whether “power-sharing” arrangements, instead of repression, are more or less likely to mitigate threats of secessionist mobilizations by disaffected, regionally concentrated minority groups. The PS-I modeling platform was used to create a virtual country “Beita,” containing within it a disaffected, partially controlled, regionally concentrated minority. Drawing on constructivist identity theory to determine behaviors by individual agents in Beita, the most popular theoretical positions on this issue were tested. Data were drawn from batches of hundreds of Beita histories produced under rigorous experimental conditions. The results lend support to sophisticated interpretations of the effects of repression vs. responsive or representative types of power-sharing. Although in the short run repression works to suppress ethnopolitical mobilization, it does not effectively reduce the threat of secession. Power-sharing can be more effective, but it also tends to encourage larger minority identitarian movements.

Despite efforts to control fraud in public assistance programs, the perception and realities of the problem persist. Serious barriers related to data collection and research methods impede the understanding of how and why fraud occurs, thereby limiting options for improving program integrity. This paper argues that, based on a complex adaptive systems perspective, social welfare fraud can be understood as a collective outcome emerging from repeated interactions among stakeholders during the routinized business processes of public assistance programs. When dealing with fraud, great attention must be paid to how it occurs and persists, not just how serious the problem is or who commits these crimes.

This chapter shows that white collar crime can be approached using an agent-based model. Fraud in public delivery programs often involves several entities that are loosely interrelated and interact dynamically. However, the crime literature has not paid sufficient attention to these characteristics, thus providing limited utility for public managers. This chapter frames a public delivery program as a complex system. The patterns of fraud in such a system are simulated using an agent-based model called FraudSim. We demonstrate that FraudSim closely replicates the statistical and spatial patterns of fraud and provides a framework for future work in this area.

The emerging field of Normative Multi-Agent
Systems has the twin goals of providing tools for simulating
human societies and coordinating activities among heterogenous
autonomous software agents in an open environment. Norms are
frequently modelled as either observable regularities or explicit
precepts. We consider arguments from analytic philosophy that
neither approach can account for the origins of norms as both are
prone to regress problems, and we note that a sample of
simulation models all assume some form of built-in normativity.
We conclude by sketching some implications for agent design.

Agent Based Modeling and Simulation (ABMS) is an effective and trustful method to solve the complex problems of today’s world. High competition in international markets, reduced product life cycle, and increased customer requirements are the key factors, which have made supply chain to be used widely in recent years. In today's complex business environment, according to the factors such as supply chain globalization, increased outsourcing, increased fluctuations in supply and demand, and short product life cycles, firms face with a greater need for supply chain risk management (SCRM). This paper aims to study the literature of ABMS to analyze SCRM since 2000 until now, specially, the newest. Dealing with the works done on ABMS and SCRM, separately, we will discuss the literature of using ABMS in SCRM. Studies show that the issue has attracted more attention, today (nowadays). Hence, it is interesting to establish the appropriate context to conduct more analyzes in the areas that currently have limitations

The Danish government has produced a public document to announce that deviations from societal values were tolerated no more. They denounced the presence of "alternative values" areas (called "parallel societies"), located mostly in areas where low or no income residents live in social/public housing (officially called "ghetto areas" or ghettoområde). Through the means of agent-based computational simulation, this paper is a first attempt to asses (a) whether it is likely for these "alternative values" areas to appear on a map, and (b) if low income or economic inactivity is a driver for their formation. Results suggest ways in which a sustainable governmental policy on "ghetto areas" shall not start from assuming divergence where there is none.

DISCLAIMER: A different version of this paper is under review, but JoS does not blind authors and this is why we believe it is fine to upload it here.

In recent years, the Internet of Things (IoT) has become well known. It has
attracted much attention thanks to its powerful application in different fields. Currently, billions of IoT devices are connected. The massive rise in the number of connected devices instantly produces further data that requires high storage and processing technology. Besides, the IoT equipment has limited processing, and storage capability did not allow data processing or even storage. Also, things cannot make smart and quick decisions to achieve their goals, and the thing ability to communicate with other things depends on service similarity and communication protocols; this will impact the performance, efficiency, development, and scalability of IoT applications. This thesis provides three main contributions, in the first contribution, we propose a Cloud-IoT approach to provide an efficient solution for the storage and analysis of data generated by IoT applications and improve IoT scalability. In order to demonstrate the viability of the proposed approach, a Cloud-IoT system for remote patient monitoring is developed.
IoT security becomes a more challenging problem due to the computing and communication limits of IoT devices make IoT more vulnerable to security and privacy attacks. Hence, as a second contribution, we propose a robust solution based on lightweight cryptography to ensure data security. Also, we have used the access control mechanism to ensure data privacy. The experimentation demonstrates that the proposed solution more suitable for IoT devices. To tap the most out of massive data streams, IoT needs real-time management and analysis of data. In order to enhance the intelligence of IoT devices and to improve our first approach, we have developed an efficient, autonomous, and real-time solution based on the agent for data management and analysis as a third contribution. The proposed system can make fast and apt decisions in an emergency case.

Agent-Based Modeling is a research method that represents theories of social behavior as computer programs of a particular kind, rather than narratives (as ethnography does) or equations (as regression models do). Like existing research methods in sociology (both qualitative and quantitative) it can be applied throughout the discipline and offers advantages for certain research questions. In particular, the approach is referred to as agent-based because the computer program unambiguously represents interactions between heterogeneous social actors while also explicitly determining their aggregate simulated consequences. This distinguishes Agent-Based Modeling from existing quantitative approaches in sociology where the relationship between aggregate associations and individual agency is often unclear. It also distinguishes the method from existing qualitative approaches that, while investigating individuals and their interactions, have no systematic techniques for establishing their aggregate consequences. Given this capability, the methodology of Agent-Based Modeling has a distinctive logic. Agent-Based Models are calibrated using data on individual behavior (for example using ethnography or laboratory experiments) and then the computer program generates simulated aggregate data. This can then be compared with equivalent real data for validation. It is the independence of these two activities that provides Agent-Based Modeling with its distinctive claim to explanatory power. The explicitly represented link between individual and aggregate respects the complexity of social systems, the phenomenon in which individuals and their simple interactions may produce surprisingly counter-intuitive aggregate outcomes. Agent-Based Models are thus particularly suitable for investigating sociological issues involving heterogeneous actors, diverse cognitive processes and social systems mediated by entities operating between the level of the individual and the aggregate (like schools and churches).

This research focuses on justifications in an audit context. An argumentation framework based on the formal model of justification logic is applied for audit dialogues in the fish industry. Different forms of justifications used in audit like net persuasive evidence, breadth of issues, and framing evidence are formalised in the proposed framework.

Estimating the effort required in resolving an incident for a production support engagement has been a long standing problem. Although, a lot of work has been done in software engineering field in estimating effort required for software development, there is no adequate body of work on production support effort estimation. Reliable effort estimates for handling each kind of incidents can have the benefits of planning the complete project engagement for many services firm which thrive on production support projects. This paper presents a framework for estimating mean effort required to resolve an incident. It takes agent based modelling approach to capture the attributes and interactions of the agents. The framework has been tested with few policy experiments to assess the amount of effort required to resolve the incidents and its impact on SLA compliance. The model has been tested with industry data and holds promise to be a reliable tool to conduct policy experiments and enhance resource utilization.

The goal of this thesis is twofold. First, intention recognition is studied from an Artificial Intelligence (AI) modeling perspective. We present a novel and efficient intention recognition method that possesses several important features: (i) The method is context-dependent and incremental, enabled by incrementally construct- ing a three-layer Bayesian Network model as more actions are observed, and in a context-dependent manner, relying on a logic programming knowledge base concern- ing the context; (ii) The Bayesian Network is composed from a knowledge base of readily specified and readily maintained Bayesian Network fragments with simple structures, enabling an efficient acquisition of the corresponding knowledge base (ei- ther from domain experts or else automatically from a plan corpus); and, (iii) The method addresses the issue of intention change and abandonment, and can appro- priately resolve the issue of multiple intentions recognition. Several aspects of the method are evaluated experimentally, achieving some definite success. Furthermore, on top of the intention recognition method, a novel framework for intention-based decision making is provided, illustrating several ways in which an ability to recognize intentions of others can enhance a decision making process.
A second subgoal of the thesis concerns that, whereas intention recognition has been extensively studied in small scale interactive settings, there is a major shortage of modeling research with respect to large scale social contexts, namely evolution- ary roles and aspects of intention recognition. Employing our intention recognition method and the tools of evolutionary game theory, this thesis explicitly addresses the roles played by intention recognition in the final outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity for assessing intentions of others in the course of social dilemmas, we show how in- tention recognition is selected by natural selection, opening a window of opportunity for cooperation to thrive, even in hard cooperation prone games like the Prisoner’s Dilemma.
In addition, there are cases where it is difficult, if not impossible, to recognize the intentions of another agent. In such cases, the strategy of proposing commitment, or of intention manifestation, can help to impose or clarify the intentions of others. Again using the tools of evolutionary game theory, we show that a simple form of commitment can lead to the emergence of cooperation; furthermore, the combination of commitment with intention recognition leads to a strategy better than either one by itself.

Объектом диссертационной работы являются количественные исследования международных отношений. Целью исследования является выявление роли количественных методов в получении максимально достоверных результатов в прикладном исследовании международных процессов. Поскольку работа сама по себе носит теоретико-методологический характер, особое внимание уделяется эпистемологии применения количественных методов анализа в международных отношениях. Речь идет о следующих методах: теория игр, математическая статистика, сетевой анализ, имитационное моделирование (системная динамика и агентное моделирование). Рассматривались также специальные прикладные методики контент- и ивент-анализа, оценки голосования в ООН и соблюдения международных обязательств, моделирования международных переговоров. Новое качество прикладного анализа необходимо для развития отечественной внешнеполитической экспертизы и научных исследований, в т.ч. в контексте интеграции РФ в международное научно-образовательное пространство.

Mesa is an agent-based modeling framework written in Python. Originally started in 2013, it was created to be the go-to tool in for researchers wishing to build agent-based models with Python. Within this paper we present Mesa's design goals, along with its underlying architecture. This includes its core components: 1) the model (Model, Agent, Schedule, and Space), 2) analysis (Data Collector and Batch Runner) and the visualization (Visualization Server and Visualization Browser Page). We then discuss how agent-based models can be created in Mesa. This is followed by a discussion of applications and extensions by other researchers to demonstrate how Mesa design is decoupled and extensible and thus creating the opportunity for a larger decentralized ecosystem of packages that people can share and reuse for their own needs. Finally, the paper concludes with a summary and discussion of future development areas for Mesa.

Today’s trends are the ever growing competition with increased
globalization and sustainability. Together with ongoing technological developments it has made it easier to demand right quality of products and services, this has also resulted in increased demands for legislation and traceability. This  A model has been created to increase knowledge of packaging logistics performance, which covers interaction between packaging system with its users. This also model includes the tool. 'Packaging Scorecard' that measures the packaging logistics performance and shows the packagingrelated
deficiencies that exist in various levels of the value chain.
Furthermore, the tool developed to enable efficient product development.
The developed model will provide support for the development of packaging strategies to change the existing systems or develop new systems. The strategies also take into account the agents involved in the supply-demand chain by demonstrating the requirements on the packaging and how small changes in the packaging system contributes to a significant improvement in the value chain. Theoretical tool enables a better integration between in picking up the desired packaging information
interacting with the agents and the right knowledge on packaging can be handled more efficiently.
New functions and roles for companies operating in the various logistics processes were identified for example the research has identified a new type of service the Network Integrator that manages integrate the supply chain with the demand chain. This function was also identified in the
packaging area and this agent named the Packaging Integrator.development has also an impact on packaging and packaging logistics.
This dissertation deals with packaging, logistics and performance when packaging interacts with users and take a standpoint by finding answers to the research question: How can a systematic and holistic approach to packaging increase the logistics performance to create efficiency and effectiveness?
The purpose of this work is to increase knowledge regarding the packaging and its interacting role with agents in the Supply-Demand Chain in order to improve Packaging Logistics Performance. Further the thesis aims to increase knowledge in the area of Packaging Logistics Performance.
Performance in packaging logistics is about doing the right thing and things right as well as position products and services relative to its competitor.
To increase understanding of the Supply-Demand Chain the tool CATWOE (Customer, Agents, Transformation processes, World view, Owners and Environment) was applied. This tool futher developed for to adapt with packaging logistics performance and complexity theory. The general methodology was action based research and case studies.
The developed model illustrates packaging systems and how they perform in order to create conditions for better and more efficient packaging logistics performance. The thesis explores and presents ways to improve the opportunities that co-operate for better packaging, and integration of value chains.
Research in theoretical contribution is to demonstrate knowledge of packaging systems, understanding the interactive roles and existing requirements in the Supply-Demand-Chain. Practical contributions were the valuable empirical knowledge and increased understanding of packaging requirements and its interactions with agent to create conditions for effectiveness, efficiency and differentiation.

The construction of computer models is becoming an increasingly useful and popular way of testing theories in the cognitive sciences. This paper will present a brief overview of the methods available for constructing and testing computer models of social phenomena such as religious beliefs and behaviors. It will focus on the importance of theoretical continuity and data replication in computer modelling while negotiating the relationship between specificity and ecological validity when models are extended into novel contexts. This paper will argue that computer modeling is an important supplement to the methodological toolbox of cognitive scientists interested in human social phenomena. However, this is only the case if developers pay close attention to research methods and theories and if the method of a model’s development is appropriate for the target phenomenon (Sun, 2006). It concludes that multi-agent AI models are the most appropriate computational tool for the study of complex social phenomena.

Four successive tropical cyclones hit the Philippines and brought damages to several towns in the provinces of Quezon and Aurora between November 14 and December 4 in 2004. The municipalities of Infanta, Real, and General Nakar were severely damaged and as a result the communities were exposed to physical risks and experienced socio-economic difficulties. To affect policies and plans in avoiding risks brought about by disasters that may happen in the future, the vulnerabilities to rainfall-induced landslide of the three most devastated barangays in Infanta, Quezon, namely Pinaglapatan, Ilog, and Magsaysay, were determined using an agent-based model.
This study determined the vulnerability of three communities based on the framework that vulnerability is influenced by the amount and intensity of rainfall received by the area in a given time, the conditions of the biophysical characteristics of the area, and the socio-economic profile of the community in the area. A time series model to capture the long-term rainfall pattern in the area was developed with consideration to the occurrences of extreme events. Biophysical studies were conducted on the study sites to determine their respective characteristics using topography (slope and elevation), land-use, and soil type as parameters. Through a respondent-type survey, the socio-economic profiles of the communities were characterized using income, livelihood, participation in social networks, and existence of community early warning systems, among others, as parameters.
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International Journal of Sciences: Basic and Applied Research (IJSBAR)(2015) Volume 23, No 1, pp 138-166
Because of the barangays’ unique locations along the path of the 2004 landslide event, their respective biophysical characteristics are different from each other. A cluster analysis on the socio-economic parameters found that the communities are clustered into low (Cluster 1) and high (Cluster 2) vulnerabilities. Vulnerability indices generated from the agent-based model showed that those households belonging to Cluster 2 in Barangay Magsaysay were the most vulnerable while those belonging to Cluster 1 in Barangay Ilog were the least.

MSc thesis. Visit the Leiden University Repository (link) for the file with appendixes and higher resolution images.

Over the past decades, space syntax has already proven to be of added value for archaeological research. However, for any researcher unfamiliar with space syntax, a considerable investment in time and effort is required before one can apply these methods on their dataset. One has to understand a complex and broad theoretical framework, and to overcome methodological problems. This can function as a barrier, preventing researchers from using these tools. This study hopes to assist those who are considering these methods, with selecting the space syntax methods that are the most suitable for analysing spatial changes in their dataset. This thesis seeks to answer the following question: Which of the four selected space syntax methods are of the most explanatory value for the analysis of changes in of the interior spaces of buildings, taking the required time and effort into consideration?
The results of four space syntax methods, access analysis, isovist analysis, visibility graph analysis, and agent analysis, are compared with each other. City block V ii in Ostia, Italy, is used as a case study. This thesis presents guidelines for applying space syntax methods that can be understood by archaeologists who do not have a background in modern architecture.
This thesis argues that a decrease occurred in the overall accessibility of the spaces within the city block, whilst the overall degree of privacy increased. Based on a comparison of the results of each method, it is argued that access analysis is of the most explanatory value for studying the effects of architectural changes. This study argues that this method is not as time intensive as visibility graph analysis, whilst allowing for a broader range of interpretations than the other three methods. The use of isovist analysis is also recommended. Despite its simplicity, it can allow for new insights on visibility. Isovist analysis could be seen as simpler and less time-consuming alternative to visibility graph analysis.

How do blockbusters dominate competition in entertainment platforms? Traditionally, entertainment industries incur in high market concentration and extreme uncertainty on product sales. Today, with the disruption of the platform revolution, they face an additional level of complexity with the typical dynamics of multi-sided organizations. The video game industry is one of the oldest digital entertainment platform markets and, despite high risks, it generated US$137.9 billion in revenue with a consumer base of more than 2 billion gamers in 2017. I take the home console market as a case study to study hit-making emergence in entertainment platform markets. I present how publishers market concentration appears within platform's 'winner-takes-most' competition. After an empirical survey on the market's history and actors behavior, I present an agent-based model that surveys platform's life-cycle on the home console sector of North America. I find peer influence in a multi-sided organization is sufficient to reproduce the industry's typical life-cycle, high market concentration, and extreme uncertainty.

The main research idea of this thesis is to develop a strategic IT investment framework using primarily military concepts as a basis.
On the one side we discuss the most influencing military thinkers in Belgian Defense Carl von Clausewitz and in the USA John Boyd (together with Carl von Clausewitz). On the other hand, the causes and consequences of a fairly new concept Network-Centric Warfare (NCW) are investigated because NCW has dramatically changed the way in which military operations are undertaken and it is based on an intensive use of Information and Communication Technology (ICT).
The first group of research questions deals with the relevance of military theories (von Clausewitz, Boyd’s OODA (Observe – Orient -Decide - Act), United States Marine Corps Command-and-Control (USMC C2) spectrum and NCW) for a contemporary company.
The second major group research questions whether it is good idea to combine Complexity Thinking (CxT) and Systems Thinking (ST) to study Complex Adaptive Systems (CAS). We believe ST is necessary in addition to CxT because ST focuses also on the design of human-made CAS (based on restrictions imposed by humans in the organization and in the environment). Special attention is given to the extension of the Cynefin Framework which is a generic sense-making framework (situational awareness). It defines different spaces each with specific characteristics for management (as IT-investments) and leadership.
The purpose of our CAS*T framework (Complex Adaptive Systems Thinking) is to determine leverage points in the organization in general and IT in particular (adapting Enterprise Architecture). Since situational awareness is essential, we propose our Intelligence Base.
Concerning the research methods, we note that specific to this study we start from the military sciences to create an IT investment framework in the business domain. This requires abstraction of military concepts to transform them into business concepts. That is why we have only used qualitative methods for this reason based on our own experiences (in Defense), literature (mainly military as previously indicated), interviews (open questions, exchanging experiences) and correspondence (article, e-mail).
We have used the scientific inquiry version of John Boyd's OODA: Observe – Orient – Formulate Hypotheses (Decide) – Test (Act).
The testing was done through publications (articles, chapters), conferences (presentation papers), interviews (feedback) and correspondence (feedback).
Keywords: Complex Adaptive System, CAS, Systems Thinking, ST, CAS*T, Cynefin Framework, Observe-Orient-Decide-Act, OODA, USMC, Command-and-Control (C2), Investments, Information Technology, Mental Models, Intelligence Base, Systemic hierarchy of leverage points.

Agent-based modeling (ABM) is a technique that allows us to explore how the interactions of heterogeneous individuals impact on the wider behavior of social/spatial systems. In this article, we introduce ABM and its utility for studying geographical systems. We discuss how agent-based models have evolved over the last 20 years and situate the discipline within the broader arena of geographical modeling. The main properties of ABM are introduced and we discuss how models are capable of capturing and incorporating human behavior. We then discuss the steps taken in building an agent-based model and the issues of verification and validation of such models. As the focus of the article is on ABM of geographical systems, we then discuss the need for integrating geographical information into models and techniques and toolkits that allow for such integration. Once the core concepts and techniques of creating agent-based models have been introduced, we then discuss a wide range of applications of agent-based models for exploring various aspects of geographical systems. We conclude the article by outlining challenges and opportunities of ABM in understanding geographical systems and human behavior.

Due to the complex nature and numerous interacting factors that contributes to the increased vulnerability of supply chains, traditional methods have been found to be inadequate for Supply Chain Risk Management (SCRM). Agent-Based Modeling and Simulation (ABMS), an agent-oriented approach to model and simulate complex adaptive systems, represents a recent development in supply chain planning that has been regarded highly appropriate for studying risk management. The objective of this paper is to provide a multi-perspective survey of the state-of-the-art agent based modeling and simulation approaches for SCRM.

In this heavily annotated article the provocative thesis is submitted that there is something fundamentally wrong with Western Sinology, or 'Chinakunde', or 'Zhongguoxue' (as distinct from 'Hanxue', which is a kind of old-fashioned philology). 'China experts' either pretend to be knowledgeable about everything related to China, in which case they cannot be taken seriously, or - eventually - admit not to be scientific all-rounders with respect to the country, in which case they cannot be called 'China experts'. The author, who graduated in Sinology from Leyden University and in economics from Erasmus University Rotterdam, not only believes that the study of China has to be taken to a higher level (a belief he expects few tenured professors of Chinese Studies/History will share); he also explains how this long overdue task can be accomplished. Sinologists should take the complexity turn. They should treat China as a 'Ganzheit' (not: 'Gesamtheit'), as a territory-bound, history-moulded and culture-soaked totality of identifiable yet interdependent (f)actors, as a whole intimately interconnected with its numerous parts, as a hypercomplex system of complex, adaptive and non-linear systems of political, military, legal, economic, financial, social, medical, educational, artistic or other nature. Firmly distancing itself from multidisciplinary research (which in practice is a matter of juxta- rather than composition), the new study of China requires a well-thought-out, balanced division of labour. Close collaboration with ICT-driven, China-oriented experts in the natural, social and human sciences willing to co-operate with each other is a sine qua non for comprehending the country that seems to be moving to the centre stage of world politics. The study of China should be mile-wide and mile-deep. The heyday of Sinology is yet to come!
An earlier version of this highly critical but undeniably constructive paper was rejected out of hand by the editors of leading 'Chinese/Asian Studies' journals. The author claims to have reason to suspect them (and other so-called China experts) of being 'bought by China'!

Massive diffusion of green technologies is significant for building a cleaner world. However, the process of technology diffusion is usually slow and complex. An in-depth understanding of the mechanism regarding green technology diffusion is an essential precondition for effectively stimulating this process. Green technology diffusion heavily involves both social and technological changes. Although existing studies have provided rich knowledge about identifying critical drivers and barriers affecting green technology diffusion, research that considers consumers' pro-environmental attitudes and green technology diffusion as an evolving system is still sparse. Aiming at exploring the co-evolution of consumers' pro-environmental attitudes and green technology diffusion, this paper builds an agent-based model that integrates the relative agreement model with technology diffusion theories to conduct a sequence of controlled numerical experiments, which progressively unveil how attitudinal and technological factors impact green technology diffusion. The main findings include that (1) improving consumers' pro-environmental attitudes is prominently beneficial to green technology diffusion and maturation; (2) technology maturity has very limited impact on consumers' first-time purchases but significantly affects consumers' satisfaction, which would further impact consumers' repeat purchases; (3) consumers that do not support green technologies frequently emerge during the evolution of attitudes (despite the high technology maturity), which corresponds to the emergence of the anti-environmental groups observed in the real world; (4) active interactions between non-adopters enable their attitudes to converge, which results in the polarization of consumers' attitudes.