Agent-based modeling

Actors in competitive environments are bound to decide and act under conditions of uncertainty because they rarely have accurate foreknowledge of how their opponents will respond and when they will respond. Just as a competitor makes a move to improve their standing on a given variable relative to a target competitor, she should expect the latter to counteract with an iterative lagged asymmetric response, that is, with a sequence of countermoves (iteration) that is very different in kind from its trigger (asymmetry) and that will be launched at some unknown point in the future (time lag). The paper explicates the broad relevance of the newly proposed concept of " iterative lagged asymmetric responses " to the social study of temporality and to fields as diverse as intelligence and counterintelligence studies, strategic management, futures studies, military theory, and long-range planning. By bringing out in the foreground and substantiating the observation that competitive environments place a strategic premium on surprise, the concept of iterative lagged asymmetric responses makes a contribution to the never-ending and many-pronged debate about the extent to which the future can be predicted.
DOI: https://doi.org/10.1177/0961463X17752652

The proposed transdisciplinary field of ‘complexics’ would bring together all contemporary efforts in any specific disciplines or by any researchers specifically devoted to constructing tools, procedures, models and concepts
intended for transversal application that are aimed at understanding and explaining the most interwoven and dynamic phenomena of reality. Our aim needs to be, as Morin says, not “to reduce complexity to simplicity, [but] to translate complexity into theory”.
New tools for the conception, apprehension and treatment of the data of experience will need to be devised to complement existing ones and to enable us to make headway toward practices that better fit complexic theories. New mathematical and computational contributions have already continued to grow in number, thanks primarily to scholars in statistical
physics and computer science, who are now taking an interest in social and economic phenomena.
Certainly, these methodological innovations put into question and again make us take note of the excessive separation between the training received by researchers in the ‘sciences’ and in the ‘arts’. Closer collaboration between these two subsets would, in all likelihood, be much more
energising and creative than their current mutual distance.  Human complexics must be seen as multi-methodological, insofar as necessary combining quantitative-computation methodologies and more qualitative methodologies aimed at understanding the mental and emotional world of people.
In the final analysis, however, models always have a arrative running behind them that reflects the attempts of a human being to understand the world, and models are always interpreted on that basis.

Using a Peircean theory of meaning, agency may be theorized in terms of flexibility and accountability, on the one hand, and knowledge and power, on the other. In this theory, residential agency, which is closest to notions such as "power" and "choice," is the degree to which one can control the expression of a sign, compose a sign-object relation, and commit to an interpretant of this signobject relation. Representational agency, which is closest to notions such as "knowledge" and "consciousness," is the degree to which one can thematize a process, characterize a feature of this theme, and reason with this theme-character relation. Agency, as a kind of social and semiotic facility, is thereby theorized as multidimensional, graduated, and distributed. This theory allows one to analyze, as concomitant phenomena, the longue durée processes that underlie relatively perduring institutions and the real-time practices that support relatively fleeting interactions. Finally, it highlights the theoretical and empirical terrain shared by linguistic anthropology, science and technology studies, political economy, and critical theory.

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).

Changes in climate, food abundance and disturbance from humans threaten the ability of species to successfully use stopover sites and migrate between non-breeding and breeding areas. To devise successful conservation strategies for migratory species we need to be able to predict how such changes will affect both individuals and populations. Such predictions should ideally be process-based, focusing on the mechanisms through which changes alter individual physiological state and behavior. In this study we use a process-based model to evaluate how Black Brant (Branta bernicla nigricans) foraging on common eelgrass (Zostera marina) at a stopover site (Humboldt Bay, USA), may be affected by changes in sea level, food abundance and disturbance. The model is individual-based, with empirically based parameters, and incorporates the immigration of birds into the site, tidal changes in availability of eelgrass, seasonal and depth-related changes in eelgrass biomass, foraging behavior and energetics of the birds, and their mass-dependent decisions to emigrate. The model is validated by comparing predictions to observations across a range of system properties including the time birds spent foraging, probability of birds emigrating, mean stopover duration, peak bird numbers, rates of mass gain and distribution of birds within the site: all 11 predictions were within 35% of the observed value, and 8 within 20%. The model predicted that the eelgrass within the site could potentially support up to five times as many birds as currently use the site. Future predictions indicated that the rate of mass gain and mean stopover duration were relatively insensitive to sea level rise over the next 100 years, primarily because eelgrass habitat could redistribute shoreward into intertidal mudflats within the site to compensate for higher sea levels. In contrast, the rate of mass gain and mean stopover duration were sensitive to changes in total eelgrass biomass and the percentage of time for which birds were disturbed. We discuss the consequences of these predictions for Black Brant conservation. A wide range of migratory species responses are expected in response to environmental change. Process-based models are potential tools to predict such responses and understand the mechanisms which underpin them.

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.

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.

This dissertation integrates ethnographic information and computational modeling to build theory about hunter-gatherer social networks and the relationships between the characteristics of those networks and patterns of variability in material culture. Key mechanisms of personal network formation (mobility, marriage, and kinship) and social learning are represented in an agent-based model which allows both system-level social networks and large-scale patterns of artifact variability to emerge from the “bottom up” through numerous human-level behaviors and interactions. This model is used to: (1) identify patterned relationships between the human-level behaviors that we can observe ethnographically and the characteristics of the system-level social networks that emerge through those behaviors; and (2) explore how the characteristics of system-level social networks are related to the patterns of variability in items of material culture whose production is mediated through those networks. Comparisons between archaeological artifact assemblages and artifact assemblages produced during model experiments are used to evaluate network-based explanations for the appearance and disappearance of stylistic regions during the Paleoindian and Early Archaic periods (ca. 11,050-8000 radiocarbon years before present) in midcontinental North America. These comparisons suggest that the appearance of stylistic regions during the Middle and Late Paleoindian periods was most likely the result of processes of stylistic drift operating across social networks that were less inter-connected than those of the Early Paleoindian period. Decreasing social connectivity across the midcontinent was probably related to an uneven distribution of population as hunter-gatherer individuals, groups, and systems responded to environmental change at the end of the Pleistocene. Population growth and the emergence of relatively homogenous environments at the beginning of the Holocene (ca. 10,000 radiocarbon years before present) would have increased social connectivity and diminished the capacity of drift processes to produce stylistically differentiated regions.

A consideration of ethnographic data, computational model results, and archaeological data suggest that changes in family-level economics coincident with subsistence intensification contributed to the emergence of social complexity among prehistoric hunter–gatherers in eastern North America by creating the conditions for a “rich get richer” scenario. Ethnographic data are used to construct a general computational model representing key person- and family-level behaviors, constraints, and decisions affecting the size and composition of hunter–gatherer families. Results from model experiments suggest that lowering the age at which children make a significant contribution to subsistence (e.g., through the broadening of the diet to include the kinds of mass-harvested, “low quality” foods that were increasingly exploited during the Archaic and Woodland periods) relaxes constraints on family size and makes large, polygynous families economically viable. Positive feedbacks between the productive and reproductive potentials of larger families produce right-tailed distributions of family size and “wealth” when the productive age of children is low and polygyny is incentivized. Size data from over 800 prehistoric residential structures suggest right-tailed distributions of family size were present during the Late Archaic through Middle Woodland periods. These distributions would have provided variability in family-based status that permitted the emergence of hereditary social distinctions.

An ever-growing number of IBMs have been developed by modellers, who aim to aid practitioners and inform a range of issues related to conservation, natural resource management, wildlife management and human socio-economic activities. The range of practitioners using IBMs to inform their decision-making processes include statutary authorities with responsibilities in environmental and natural resource management, non-governmental organizations such as conservation charities, and those interested in the sustainable use of natural resources. Thanks to advances in computational power, data availability and ecological theory, increasingly complicated, sophisticated IBMs can be produced. Yet this does not mean that these models will be more useful in informing environmental decision-making. IBMs typically require specialist computational knowledge to build and refine the model, and analyse the model outputs, and so practitioners are unlikely to have the requisite skills to use IBMs directly. Conversely, practitioners may have greater knowledge of the system being modelled, so their input into the modelling process is highly desirable. Thus, it is important that modellers and practitioners collaborate effectively to develop models which can address the key questions that practitioners are interested in. In this paper we highlight the uses of IBMs in environmental decision-making, identify potential obstacles to their successful use, and discuss how such obstacles can be overcome. We aim to help practitioners understand the potential benefits of IBMs, and to help modellers to understand how to develop IBMs which will better aid practitioners and inform environmental management and policy. We refer to a coastal bird IBM case study, from which general lessons can be learned.

Computer models of the economy are regularly used to predict economic phenomena and set financial policy. However, the conventional macroeconomic models are currently being reimagined after they failed to foresee the current economic crisis, the outlines of which began to be understood only in 2007-2008. In this article we analyze the most prominent of this reimagining: Agent-Based models (ABMs). ABMs are an influential alternative to standard economic models, and they are one focus of complexity theory, a discipline that is a more open successor to the conventional chaos and fractal modeling of the 1990s. The modelers who create ABMs claim that their models depict markets as ecologies, and that they are more responsive than conventional models that depict markets as machines. We challenge this presentation, arguing instead that recent modeling efforts amount to the creation of models as ecological machines. Our paper aims to contribute to an understanding of the organizing metaphors of macroeconomic models, which we argue is relevant conceptually and politically, e.g., when models are used for regulatory purposes.

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.

The general objective of our research is to develop the complex agent-based simulation of ancient Celtic society population, food production and economics. To achieve this we proposed the agent-based agricultural model of land suitability for miscellaneous farming systems. The behaviour of agents is controlled by a fuzzy rule-based system. The system encodes farmers’ agricultural knowledge as regards the decision where to apply specific farming method. Corresponding linguistic variables describe (1) the distance of individual land patches from the household, (2) the slope of the land, (3) the yield of individual land patches and (4) total harvest as a percentage of inhabitants' annual nutritional requirement. To prevent high-frequency spatial changes in land suitability caused by time-dependent stochastic nature of the crop yields, our model performs result smoothing using spatial signal filtering. The model is tested on the GIS data.

Agent-based models and self-organisation: addressing common criticisms and the role of agent-based modelling in urban planning While planners have long been aware that the world they act upon is complex, there is now growing interest in using concepts from the complexity sciences – such as self-organisation – to explain cities and urban change. Agent-based models (ABMs) can be invaluable tools in this quest, but they are often met with mistrust, and face some valid objections. This paper attempts to address some of the criticism, namely that ABMs offer simplistic, individualist and trivial explanations for urban processes, that they cannot be validated and that they represent the view that top-down planning is unnecessary. The paper argues that if ABMs are to be used in a new science of cities they need to be designed as part of a conversation between practitioners, policy-makers and academics.

Recent decades have seen great advances in ecological modelling and computing power, enabling ecologists to build increasingly detailed models to more accurately represent ecological systems. To better inform environmental decision-making, it is important that the predictions of these models are expressed in simple ways that are straightforward for stakeholders to comprehend and use. One way to achieve this is to predict threshold values for environmental perturbations (e.g. climate change, habitat modification, food loss, sea level rise) associated with negative impacts on individuals, populations, communities or ecosystems. These thresholds can be used by stakeholders to inform management and policy. In this paper we demonstrate how this approach can use individual-based models of birds, their prey and habitats, to provide the evidence-base for coastal bird conservation and shellfishery management. In particular, we show how such models can be used to identify threshold values for perturbations of food abundance that can impact negatively on bird populations. We highlight how environmental thresholds could be used more widely to inform management of species and habitats under environmental change.

Reservoir systems with multiple operators can benefit from coordination of operation policies. To maximize the total benefit of these systems the literature has normally used the social planner’s approach. Based on this approach operation decisions are optimized using a multi-objective optimization model with a compound system’s objective. While the utility of the system can be increased this way, fair allocation of benefits among the operators remains challenging for the social planner who has to assign controversial weights to the system’s beneficiaries and their objectives. Cooperative game theory provides an alternative framework for fair and efficient allocation of the incremental benefits of cooperation. To determine the fair and efficient utility shares of the beneficiaries, cooperative game theory solution methods consider the gains of each party in the status quo (non-cooperation) as well as what can be gained through the grand coalition (social planner’s solution or full cooperation) and partial coalitions. Nevertheless, estimation of the benefits of different coalitions can be challenging in complex multi-beneficiary systems. Reinforcement learning can be used to address this challenge and determine the gains of the beneficiaries for different levels of cooperation, i.e., non-cooperation, partial cooperation, and full cooperation, providing the essential input for allocation based on cooperative game theory. This paper develops a game theory–reinforcement learning (GT–RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria. As the first step to underline the utility of the GT–RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.

SHEEP (Simulating Hominin Expansion in the Early Pleistocene) is an Agent-based model (ABM) of the first Out of Africa, designed to explore the spatial pattern of Lower Palaeolithic site distribution in Europe. The SHEEP model uses a deterministic environmental approach to evaluate potential routes into Europe and their impact on the pattern of site distribution in the eastern and western parts of the continent. The model consists of three main elements: 1) population growth, 2) a spatial spreading process and 3) the friction map, which includes both environmental and topographic information. The impact of different geographical projections on the outcome of the model has been evaluated. The results show that we are unable to reproduce the observed spatial pattern of European sites from differences in dispersal routes. This case study showcases the use of ABM as a simple and efficient tool for hypotheses testing with a high potential for archaeological applications.

In this paper, we describe a novel system for learning and adapting type-2 fuzzy controllers for intelligent agents that are embedded in Ubiquitous Computing Environments (UCE). Our type-2 agents learn online in a non intrusive manner and in a life long learning mode the user behaviours to control the UCE on the user’s behalf. We have performed unique experiments in which the type-2 intelligent agent has learnt and adapted online to the user’s behaviour during a stay of five days in the intelligent Dormitory (iDorm) which is a real ubiquitous computing environment test bed. We will show how our type-2 agents deal with the uncertainty and imprecision present in UCE to give a very good response that outperforms the type-1 fuzzy agents while using a smaller number of rules.

Research into the processes that form the archaeological record has become an important component of archaeological practice because formation processes are a key link between the materials that archaeologists study and prehistoric societies that they seek to understand. Computational modeling is a comparatively new technology with potential to provide new insights into the dynamics of human societies, but which has been minimally applied so far in the study of archaeological formation processes. We use computational modeling as an experimental environment to examine processes that form the archaeological record of lithic assemblages. This is especially important for lithics as it is a largely extinct technology and we cannot directly observe the accumulation of lithic assemblages over time frames comparable to those represented in the archaeological record. We systematically evaluate the individual and combined effects of the length of stay at sites, raw material distribution, differences in activities performed with lithics, and movement patterns on lithic assemblages that accumulate over different time intervals. Not surprisingly, increased access to raw material decreases the frequency of retouched artifacts in assemblages, while tasks that require more lithic use produce assemblages with higher retouch frequencies. While length of stay affects the density of lithic accumulations at sites, it has little effect on assemblages composition. Mobility patterns alone have limited impact on assemblage composition. However, mobility coupled with place provisioning or individual provisioning, associated with logistical and residential mobility strategies respectively have significant impacts on assemblage composition consistent with prior empirical studies. Counterintuitively, the artifact palimpsests of multiple occupations that characterize most archaeological deposits may provide better information about human ecology and changing adaptations than assemblages that represent snapshots of a single or a few occupations. This work provides valuable new, quantitative insights into the information about past human social, ecological, and technological practices embedded in lithic assemblages.

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'!

The research proposes and evaluates a structured approach to the development of complex multiorganizational incentive architectures for complex sustainable supply chains. It is proposed that Model-Based Systems Engineering (MBSE), a process for software and systems development, combined with SysML, a structured systems modeling standard, can be combined to design and test complex multiorganizational supply chain incentive architectures. Such an approach is a suitable for architecting complex 'system-of-systems' architectures such as 'Smart Cities'. A review of trends in associated literature is conducted to ground research design and scope: 1) Perspectives concerning sustainable, multistakeholder resource supply chain management; 2) Formal systems engineering and modeling practices (as relevant to multi-organizational and multi-stakeholder economic systems); and 3) Multi-stakeholder incentive architectures for sustainable resource value chain orchestration.

In this paper I examine the epistemic function of agent-based models (ABMs) of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni's (2005) classification of ABMs into case-based models, typifications and theoretical abstractions , I argue that proposed ABMs of scientific inquiry largely belong to the third category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman's (2010) ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM (Borg et al., 2017b, 2018a,b), which provides insights into potential mechanisms underlying the efficiency of scientific inquiry.

En éste trabajo se propone la arquitectura de un Sistema Tutor Inteligente que considera el estilo de aprendizaje del alumno y la Educación Basada en Competencias, integrando un agente inteligente para la selección de las estrategias de enseñanza-aprendizaje. La arquitectura plantea innovaciones en la representación del módulo tutor y del módulo de conocimiento. En particular, en el módulo tutor se incorpora un agente selector, el cual se encargará de elegir los contenidos considerando las estrategias de enseñanza que favorezcan el estilo de aprendizaje del alumno.

Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.