Agent-based Modeling Methodology Research Articles

Utilizing Amphibious AGVs to Optimize Container Transshipment for Deep Sea and Hinterland Operations

Transshipment is a key component of modern-day shipping logistics. Container supply chains rely on tran-shipment hubs to access remote locations. With globalisation driving growth in container trade, maritime congestion is rising at container terminals in ports worldwide. This is expected to worsen as demand con-tinues to grow. This research explores novel maritime equipment designs that can contribute to solving problems in the trans-shipment chain. One such idea is that of the Amphibious Automated Guided Vehicle, an innovative concept that travels on both land and sea. Envisioned as a tool to minimise the rehandling of containers, the Amphibious AGV forms the heart of the new changes that this research proposes for the fu-ture of trans-shipment. Complementing swifter trans-shipment, the research also proposes complementary design concepts such as floating terminals to add more flexibility for container ships and Amphibious AGVs applied to exchange containers offshore. To validate these ideas, an agent-based modelling methodology was used and replicated in the environment of the Hong Kong- Pearl River Delta. This work, therefore, opens up an intriguing future scope for maritime transshipment that is both sustainable and adaptable while also discussing limitations and concerns that need to be carefully considered.

A systematic review of agent-based modelling in the circular economy: Insights towards a general model

Circular Economy (CE) is a popular topic for governments and businesses around the world; yet, only a few comprehensive and economy-wide frameworks exist, and the consequences of the CE on economic systems stay unclear. With this systematic review, we put under scrutiny the existing contributions to Circular Economy (CE) that apply the Agent-based modelling methodology. There is an open gap in the CE literature regarding the use of ABM. The research question that guides this systematic review concerns the potential benefit of ABM for CE and how to use this methodology in the context of CE. We put in evidence three thematic areas, two agents and one process, namely producers, i.e. firms and industrial systems, consumers, i.e. households and waste disposal, and the diffusion of innovation. We infer that the former three thematic strands of literature can be further synthetized together to form a general model of the Circular Economy. This development is crucial to properly evaluate how the agent's heterogeneity affects the diffusion and the consequences of the adoption of CE practices on the economy. Research has widely applied ABM simulations to consider the impact of heterogeneity amongst individuals and their behavioural interactions on the evolution of complex systems, yet very little did it systematically about CE. Our results complement those of Computable General Equilibrium models. The review provides an interpretative framework, suggests valuable future research directions within the new comprehensive thematic area, and contributes to the theoretical and managerial discussion on agent-based modelling in the circular economy.

Agent-based modeling methodology and temporal simulation for Natech events in chemical clusters

In chemical clusters, technological accidents triggered by natural events (Natech events) can lead to the failure of large number of units and cause catastrophic consequences. Previous studies on Natech events give primary concerns on the static analysis at small-scale region with few thoughts the impact of various safety barriers. From the perspective of the large-scale region and temporal characteristics, an agent-based modeling methodology is proposed to present the dynamic evolution process. All the units associated with the evolution are divided into natural hazard agent, hazard-affected facility agent, safety barrier agent, and escalation factor agent to reduce the computational complexity. The time-dependent state transition mechanism of each agent is analyzed to represent the evolution process, simultaneously, the temporal effect of natural hazards, safety barrier and multi escalation factors is considered by utilization of time steps. Moreover, Monte Carlo simulation is adopted to quantify the uncertainties accompanying an accident evolution. The proposed methodology is demonstrated on a complex chemical system in the large-scale region. The results of simulation show that the number of failed hazard-affected facilities in the Natech events scenario will cyclically increase and keep constant, with strong temporal characteristic. The intensity of natural hazards and the probability of failure of safety barriers make a big impact on the temporal characteristic. The results are valuable to support natural hazard prevention in chemical cluster.

Investigating immersion and migration decisions for agent-based modelling: A cautionary tale

Background Agent-based modelling provides an appealing methodological choice for simulating human behaviour and decisions. The currently dominant approaches based on static transition rates or unverified assumptions are restrictive, and could be enhanced with insights from cognitive experiments on actual decision making. Here, one common concern is that standard surveys or experiments may lack ecological validity, limiting the extent to which research findings can be generalised to real-life settings. For complex, highly emotive decision-making scenarios, such as those related to migration, the typically used short, methodical survey questions may not appropriately map onto complex real-world decisions of interest. Immersive contexts may offer more accurate representations of reality, potentially enhancing the usefulness of experimental information in multi-disciplinary modelling endeavours. Methods This pre-registered study of migration decisions, aimed at informing a multi-disciplinary construction of an agent-based model of migration, presents a choice-based interactive fiction game in which players make migration decisions to advance through a story. Participants (N = 1000 Prolific users) were randomly assigned to one of four experimental conditions, three involving different renditions of the game attempting to create immersion, with the last condition presenting the decisions in standard survey format. Results Although addressing the lack of ecological validity in survey data is important for improving agent-based modelling methodology, the experimental design used to tackle this issue, while responding directly to modelling needs, proved too complex. The created experimental conditions ended up too distinct from each other, involving stimuli that differed in quantity and content. This introduced several unintended and uncontrolled confounds, making it impossible to meaningfully interpret the results of this experiment on its own. Our results act as a cautionary tale for agent-based modellers, highlighting that the modelling needs should not override the principles of experimental design, and provide motivation for more rigorous research on this topic.

Investigating immersion and migration decisions for agent-based modelling: A cautionary tale

Background: Agent-based modelling provides an appealing methodological choice for simulating human behaviour and decisions. The currently-dominant approaches based on static transition rates or unverified assumptions are restrictive, and could be enhanced with insights from cognitive experiments on actual decision making. Here, one common concern is that standard surveys or experiments may lack ecological validity, limiting the extent to which research findings can be generalised to real-life settings. For complex, highly emotive decision-making scenarios, such as those related to migration, the typically-used short, methodical survey questions may not appropriately map onto complex real-world decisions of interest. Immersive contexts may offer more accurate representations of reality, potentially enhancing the usefulness of experimental information in multi-disciplinary modelling endeavours. Methods: This pre-registered study of migration decisions, aimed at informing a multi-disciplinary construction of an agent-based model of migration, presents a choice-based interactive fiction game in which players make migration decisions to advance through a story. Participants (N = 1000 Prolific users) were randomly assigned to one of four experimental conditions, three involving different renditions of the game attempting to create immersion, with the last condition presenting the decisions in standard survey format. Results: Although addressing the lack of ecological validity in survey data is important for improving agent-based modelling methodology, the experimental design used to tackle this issue, while responding directly to modelling needs, proved too complex. The created experimental conditions ended up too distinct from each other, involving stimuli that differed in quantity and content. This introduced several unintended and uncontrolled confounds, making it impossible to meaningfully interpret the results of this experiment on its own. Our results act as a cautionary tale for agent-based modellers, highlighting that the modelling needs should not override the principles of experimental design, and provide motivation for more rigorous research on this topic.

An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets.

Using agent-based modeling in routine dynamics research: a quantitative and content analysis of literature

This paper presents an overview of all scientific contributions using agent-based modeling (ABM) methodology in routine dynamics research. That is a specialized area of study and our extensive literature search revealed only a total of 12 contributions. We did a quantitative analysis of these published literature using co-authorship, cross-citation and bibliographic coupling methods. We then complemented the overview with a content analysis of substantial focus of these literatures. We summarized their findings and showed how ABM is applied in routine dynamics research. By elaborating on what has been done, we provide an overview of this newly emerging research field and present possible directions for future work. Even though there are only a few publications in this research field, we expect that dialogue between ABM modelers and routine scholars would be beneficial to promote the development in this area. It would be conducive to advancing the understanding of complex internal structure, processes and dynamics of organizational routines.

An Agent-Based Model for the Socio-Economic Monitoring of Vis-itor Streams A Study Using the Example of the Harz National Park, Germany

The Harz National Park (NP) offers a well-developed path network with a total length of 813 km, as well as a high number of starting and destination points for hikes. It was visited by about 1.7 million visitors in 2014. Due to its large extent, an accurate measurement of visitor numbers and their spatio-temporal distribution is not feasible. This paper demonstrates the possibility of simulating visitor streams around Mt. Brocken buy using the agent-based model (ABM) methodology. The GAMA v1.7 RC2 modelling environment was chosen, be-cause it has very extensive spatial operators and simulation tasks, combined with an easy-to-understand modelling language. To reduce the simulation effort, a model reduction factor MRF = 10 was tested successfully: no significant change to the model was required. After an initial parameterization, a sensitivity analysis was conducted, with the results in-cluded in the final calibration. The observed error value was significantly reduced, from RMSEparam=0,3817 to RMSEcalib=0,1069, and therefore the model was applied to the study area. For the final validation, visitor numbers from other, independent investigations were used. In addition to the 12 main routes and 7 hotspots identified, basic socio-economic indicators emerged. Hikers’ changes in behaviour according to framework conditions were analysed, demonstrating the impact of current measures on the reduction of path use. The final result is a flexible and expandable baseline model, which provides a realistic picture of the spatial distribution of hikers in the study area and additional key so-cio-economic figures.

An Agent-Based Model for the Socio-Economic Monitoring of Vis-itor Streams A Study Using the Example of the Harz National Park, Germany

The Harz National Park (NP) offers a well-developed path network with a total length of 813 km, as well as a high number of starting and destination points for hikes. It was visited by about 1.7 million visitors in 2014. Due to its large extent, an accurate measurement of visitor numbers and their spatio-temporal distribution is not feasible. This paper demonstrates the possibility of simulating visitor streams around Mt. Brocken buy using the agent-based model (ABM) methodology. The GAMA v1.7 RC2 modelling environment was chosen, be-cause it has very extensive spatial operators and simulation tasks, combined with an easy-to-understand modelling language. To reduce the simulation effort, a model reduction factor MRF = 10 was tested successfully: no significant change to the model was required. After an initial parameterization, a sensitivity analysis was conducted, with the results in-cluded in the final calibration. The observed error value was significantly reduced, from RMSEparam=0,3817 to RMSEcalib=0,1069, and therefore the model was applied to the study area. For the final validation, visitor numbers from other, independent investigations were used. In addition to the 12 main routes and 7 hotspots identified, basic socio-economic indicators emerged. Hikers’ changes in behaviour according to framework conditions were analysed, demonstrating the impact of current measures on the reduction of path use. The final result is a flexible and expandable baseline model, which provides a realistic picture of the spatial distribution of hikers in the study area and additional key so-cio-economic figures.

Identification of walking human model using agent-based modelling

Abstract The interaction of walking people with large vibrating structures, such as footbridges and floors, in the vertical direction is an important yet challenging phenomenon to describe mathematically. Several different models have been proposed in the literature to simulate interaction of stationary people with vibrating structures. However, the research on moving (walking) human models, explicitly identified for vibration serviceability assessment of civil structures, is still sparse. In this study, the results of a comprehensive set of FRF-based modal tests were used, in which, over a hundred test subjects walked in different group sizes and walking patterns on a test structure. An agent-based model was used to simulate discrete traffic-structure interactions. The occupied structure modal parameters found in tests were used to identify the parameters of the walking individual’s single-degree-of-freedom (SDOF) mass-spring-damper model using ‘reverse engineering’ methodology. The analysis of the results suggested that the normal distribution with the average of μ = 2.85Hz and standard deviation of σ = 0.34Hz can describe human SDOF model natural frequency. Similarly, the normal distribution with μ = 0.295 and σ = 0.047 can describe the human model damping ratio. Compared to the previous studies, the agent-based modelling methodology proposed in this paper offers significant flexibility in simulating multi-pedestrian walking traffics, external forces and simulating different mechanisms of human-structure and human-environment interaction at the same time.

Empirically grounded agent-based models of innovation diffusion: a critical review

Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

Cross-border effects of capacity mechanisms in interconnected power systems

The cross-border effects of a capacity market and a strategic reserve in interconnected electricity markets are modeled using an agent-based modeling methodology. Both capacity mechanisms improve the security of supply and reduce consumer costs. Our results indicate that interconnections do not affect the effectiveness of a capacity market, while a strategic reserve is affected negatively. The neighboring zone may free ride on the security of supply provided by the zone implementing a capacity mechanism. However, a capacity market causes crowding out of generators in the energy-only zone. A strategic reserve implemented by this region could aid in mitigating this risk.

An Agent-based Model to Study the Evolution of Service Systems through the Service Life Cycle

We introduce a unified concept of service life cycle (SLC) for the study of service system evolution. The need for this research arises from the fact that studying service ecosystems has become a critical need in the modern service economy. Adopting the view that a service ecosystem is a complex adaptive system of interacting service systems, we propose Agentbased Modeling methodology as a potential candidate for service research. Our agent-based model simulates an artificial market, in which one hypothetical service is being offered and customer agents interact with service provider agents to co-create value. The design of the model takes inputs from the service dominant logic, Kauffman’s NKCS model and the ISPAR model of service system interactions. The simulation results reveal that the evolution of service providers follows a life cycle pattern, which we call the Service Life Cycle. We propose the SLC to be used as a reference to evaluate the performance of service providers in a service ecosystem, initiating a novel S-D logic based discussion on sustainability of business firms.

Insights from the Application of an Agent-Based Computer Simulation as a Coaching Tool for Top-Level Rugby Union

We discuss an attempt to use a computer simulation as a method to allow coaches to develop and test tactics in the sport of rugby union. Such approaches are common in military science, and appear promising as a method in sport. An agent-based modelling methodology is used, where agents are software entities which represent players and are capable of making decisions for themselves. We discuss how such an approach allows a coach to estimate the potential success of a planned move, by creating a simulation in which the defence responds in a realistic and unscripted manner, which ultimately may help to identify ideal attacking or defensive patterns for a given circumstance. This in turn reduces the need for extended experimentation with new moves during training sessions. We give a description of how the model was employed by a top international rugby team, the All Blacks, and what lessons were learned.

I-ABM: combining institutional frameworks and agent-based modelling for the design of enforcement policies

Computer science advocates institutional frameworks as an effective tool for modelling policies and reasoning about their interplay. In practice, the rules or policies, of which the institutional framework consists, are often specified using a formal language, which allows for the full verification and validation of the framework (e.g. the consistency of policies) and the interplay between the policies and actors (e.g. violations). However, when modelling large-scale realistic systems, with numerous decision-making entities, scalability and complexity issues arise making it possible only to verify certain portions of the problem without reducing the scale. In the social sciences, agent-based modelling is a popular tool for analysing how entities interact within a system and react to the system properties. Agent-based modelling allows the specification of complex decision-making entities and experimentation with large numbers of different parameter sets for these entities in order to explore their effects on overall system performance. In this paper we describe how to achieve the best of both worlds, namely verification of a formal specification combined with the testing of large-scale systems with numerous different actor configurations. Hence, we offer an approach that allows for reasoning about policies, policy making and their consequences on a more comprehensive level than has been possible to date. We present the institutional agent-based model methodology to combine institutional frameworks with agent-based simulations). We furthermore present J-InstAL, a prototypical implementation of this methodology using the InstAL institutional framework whose specifications can be translated into a computational model under the answer set semantics, and an agent-based simulation based on the jason tool. Using a simplified contract enforcement example, we demonstrate the functionalities of this prototype and show how it can help to assess an appropriate fine level in case of contract violations.

An agent based model for simulating the spread of sexually transmitted infections

This work uses agent-based modelling (ABM) to simulate sexually transmitted infection (STIs) spread within a population of 1000 agents over a 10-year period, as a preliminary investigation of the suitability of ABM methodology to simulate STI spread. The work contrasts compartmentalized mathematical models that fail to account for individual agents, and ABMs commonly applied to simulate the spread of respiratory infections. The model was developed in C++ using the Boost 1.47.0 libraries for the normal distribution and OpenGL for visualization. Sixteen agent parameters interact individually and in combination to govern agent profiles and behaviours relative to infection probabilities. The simulation results provide qualitative comparisons of STI mitigation strategies, including the impact of condom use, promiscuity, the form of the friend network, and mandatory STI testing. Individual and population-wide impacts were explored, with individual risk being impacted much more dramatically by population-level behaviour changes as compared to individual behaviour changes.

Development of an Agent-Based Modeling Methodology for an Industrial Byproduct Exchange Network Design

Byproduct exchange networks are attracting increasing attention to allow the use of byproducts that would have otherwise been discarded. To take full advantage of the benefits, it is necessary to consider the interacting features between the participating byproduct buyers and sellers in terms of both economic and environmental perspectives. This paper proposes an agent-based modeling framework as an alternative. A case of water reuse networks in an iron and steel manufacturing industrial park is presented to illustrate the applicability of the proposed framework.

Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach

Applying agent-based modeling and simulation (ABMS) methodology, this paper analyzes the impact of alternative production–sales policies on the diffusion of a new generic product and the generated NPV of profit. The key features of the ABMS model, that captures the marketplace as a complex adaptive system, are: (i) supply chain capacity is constrained; (ii) consumers’ new product adoption decisions are influenced by marketing activities as well as positive and negative word-of-mouth (WOM) between consumers; (iii) interactions among consumers taking place in the context of their social network are captured at the individual level; and (iv) the new product adoption process is adaptive. Conducting over 1 million simulation experiments, we determined the “best” production–sales policies under various parameter combinations based on the NPV of profit generated over the diffusion process. The key findings are as follows: (1) on average, the build-up policy with delayed marketing is the preferred policy in the case of only positive WOM as well as the case of positive and negative WOM. This policy provides the highest expected NPV of profit on average and it also performs very smoothly with respect to changes in build-up periods. (2) It is critical to consider the significant impact of negative word-of-mouth in choosing production–sales policies. Neglecting the effect of negative word-of-mouth can lead to poor policy recommendations, incorrect conclusions concerning the impact of operational parameters on the policy choice, and suboptimal choice of build-up periods.

From the Special Issue Editors: Agent‐Based Modeling of Innovation Diffusion*

I n recent years, agent-based modeling has gained popularity in many scientific areas as a modeling tool capable of capturing the dynamics that arise from the interactions between individuals (e.g, Gilbert and Troitzsch, 2005). The perspective of this method, rooted in complexity theory, is that phenomena at the macro—or aggregated—level can be understood as emerging from interactions between individuals at the micro level. On their turn, these macro phenomena affect the behavioral context at the micro level, a process referred to as downward causation (e.g., Emmeche, K ppe, and Stjernfelt, 2000). Especially in the field of innovations, agent-based modeling offers a promising methodology, since interactions between large populations of consumers, groups of competing and collaborating firms and businesses, and between firms and consumers are relevant in most markets (e.g., Garcia, 2005; Gilbert, Jager, Deffuant, and Adjali, 2007). In particular, innovation diffusion is a phenomenon where interactions at the micro level between adopters and influencers are of critical importance. From the initial introduction of a new product or service, where involved consumers may spread information on product characteristics, to later stages where norms may evolve and support the further diffusion of the product/service in the market, social interactions play a pivotal role in the success (or failure) of the new product or service. It is the overall rate of diffusion at the macro level that is the major concern of many organizations, but macro-level outcomes emerging from adopter interactions at the micro level are not always predictable. Due to the interesting phenomena of feedbacks, a coincidental choice by a small group of adopting agents may cause an avalanche of successive interactions, giving rise to self-amplifying effects (nonlinear behavior) that cannot be predicted from detailed adoption data. Examples of this occurring include fads, fashions, and network externalities where the number of adopters exponentially improves consumer satisfaction (e.g., Janssen and Jager, 2001). Lack of predictability in knowing which products will take off and which will languish confronts managers with a challenge: how to capture the complex nature of innovation diffusion? What can be learned from interactions at the micro level that can help explain, manage, and sometimes predict processes at the macro level? This special issue demonstrates how agent-based modeling can be a tool for capturing micro-level individuals’ underlying decision processes and mimicking dynamic social effects observed at the macro level in the marketplace. Criticisms have arisen about agent-based models (ABM) as being ‘‘toy models’’ and unrepresentative of real phenomena. A goal of this special issue is to demonstrate that agent-based models can and should be grounded within a real market issue to go beyond the level of toy models. Ultimately, the value of agentbased simulation models, both for practitioners and for management science, resides in their capacity to contribute to our understanding and management of real markets. After an extensive review process, we selected five papers that we felt help to further develop the agent-based modeling methodology by incorporating real market research questions regarding the diffusion of innovations. Broekhuizen, Delre, and Torres focus on how social influences in the motion picture industry cause market inequalities between different countries with respect to the success of new movie releases; Zhang (Tao) and Nuttall provide a model to simulate the diffusion of smart electricity meters in Great Britain as a function of different policy options; Ahrweiler, Pyka, and Gilbert study the dynamics of innovation networks in knowledgeintensive industries and reveal how the presence of universities in such networks contributes to the quantity and speed of innovation; van Eck, Jager, and Leeflang investigate the role of opinion leaders in the diffusion of an online gaming community of children; and Zhang (Ting), Gensler, and Garcia investigate consumer adoption of alternative fuel vehicles in the Address correspondence to: Dr. Wander Jager, Associate Professor of Marketing, Faculty of Economics and Business, Nettelbosje 29747 AE Groningen, The Netherlands. E-mail: W.Jager@rug.nl.

AGENT-BASED MODELING: USE WITH NECESSARY CAUTION

Agent-based modeling—a tool employing multiple interacting agents to reveal emergent properties of systems that are not properties of the individual agents themselves1—has made significant in-roads into many social science disciplines,2 including public health. I congratulate Dennis Gorman and his colleagues on their effort to leverage these new tools to address specific public health concerns.3 However, given the novelty of agent-based modeling methodology and the resistance of those favoring traditional analytic methods,4 great care in application is necessary to increase the likelihood of acceptance. In this spirit, I offer a few concerns. Most important, it is imperative to be clear about what agent-based models have to offer. Gorman et al. claim that their “models demonstrate that the basic dynamics underlying social influences on drinking behavior are shaped by contacts between drinkers and focused by characteristics of drinking environments.”3(p2055) The offered models demonstrate no such thing. At most, the models provide insight into the spatial dynamics of agents following programmed rules that respond stochastically to other agents in a highly stylized environment.5 Whether this teaches us about drinking behavior is an empirical question.4 Similarly, the “bar”3(p2058) is nothing more than a probabilistic sink that guarantees to attract the “drinkers” who in no way “chose to spend a greater portion of their time at this site”3(p2059) (emphasis added). Ecological validity and simple face validity are also important concerns. In one instance, Gorman et al. sought to identify ecological components of drinking behavior that they suggest might be thought of as a college population that moves from a specific site with a given probability each day, operationalized as a random walk.3(p2056) However, each of these students will have his or her own sink in the form of an apartment or a dorm room that he or she should return to (at least on most days). Gorman et al.’s random-walk method quickly produces probabilistic homelessness. Finally, precision is required in specifying models and reporting results. For example, a lattice can be bordered or unbordered (i.e., moving off the right side puts you on the left side) and conversion can be asynchronous or synchronous (all agents determine the new agent type before all agents convert together). I replicated a family of models with NetLogo 3.1.1 (Center for Connected Learning and Computer-Based Modeling, Evanston, Ill) to determine the likely characteristics of Gorman et al.’s models, with good results (Figure 1 ▶). (The “bar” model runs 20000 periods in approximately 40 seconds on a 1.6 Ghz Pentium processor; models are available from the author upon request.) This replication also helped me to sort out the mathematical error in Gorman et al.’s Figure 1 ▶3(p2056) (The ordinary proportion for conversion from susceptibles to drinkers, FIGURE 1— Replication of Gorman et al.’s3 primary model: P = .3; γ = .3; ρ = .3 on an unbordered lattice with synchronous conversions. shown on the state diagram, rather than double this proportion as specified in the body text of the diagram is what I believe was intended.) With this replicated model, I was also able to investigate concerns about the authors’ claim that with a “bar” on the lattice, susceptibles leveled “off at a constant nonzero value.”3(p2059) Analytically, this could not be the case. Indeed, the clustering of drinkers makes the conversion of a susceptible on a random walk a low-probability event, but the susceptibles do eventually go to zero (taking a mean of 10 822 periods over 50 runs), a characteristic missed because of incorrect assumptions of scale (Figure 2 ▶). FIGURE 2— Replication of Gorman et al.’s3 “bar” model: P = .3; γ = .3; ρ = .3. For agent-based modeling to meet its promise, practitioners should resist the temptation to overstate the implications of model outcomes, carefully design agent-based models with an eye toward ecological validity, and provide precise specifications and results based on families of models subjected to repeated tests.