JASSS logo


28 articles matched your search for the keywords:
Electric Vehicle, Diffusion, Mixed Logit, Vehicle Choice, Network Effects

Taking into Account the Variations of Neighbourhood Sizes in the Mean-Field Approximation of the Threshold Model on a Random Network

Sylvie Huet, Margaret Edwards and Guillaume Deffuant
Journal of Artificial Societies and Social Simulation 10 (1) 10

Kyeywords: Aggregate; Individual-Based Model; Innovation Diffusion; Mean Field Approximation; Model Comparison; Social Network Effect
Abstract: We compare the individual-based \'threshold model\' of innovation diffusion in the version which has been studied by Young (1998), with an aggregate model we derived from it. This model allows us to formalise and test hypotheses on the influence of individual characteristics upon global evolution. The classical threshold model supposes that an individual adopts a behaviour according to a trade-off between a social pressure and a personal interest. Our study considers only the case where all have the same threshold. We present an aggregated model, which takes into account variations of the neighbourhood sizes, whereas previous work assumed this size fixed (Edwards et al. 2003a). The comparison between the aggregated models (the first one assuming a neighbourhood size and the second one, a variable one) points out an improvement of the approximation in most of the value of parameter space. This proves that the average degree of connectivity (first aggregated model) is not sufficient for characterising the evolution, and that the node degree variability has an impact on the diffusion dynamics. Remaining differences between both models give us some clues about the specific ability of individual-based model to maintain a minority behaviour which becomes a majority by an addition of stochastic effects.

How Realistic Should Knowledge Diffusion Models Be?

Jean-Philippe Cointet and Camille Roth
Journal of Artificial Societies and Social Simulation 10 (3) 5

Kyeywords: Agent-Based Simulation, Complex Systems, Empirical Calibration and Validation, Knowledge Diffusion, Model Comparison, Social Networks
Abstract: Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.

Rethinking Lock-in and Locking: Adopters Facing Network Effects

Marc R.H. Roedenbeck and Barnas Nothnagel
Journal of Artificial Societies and Social Simulation 11 (1) 4

Kyeywords: Path Dependence, Gaussian Distributed Adopters, Network Effects, Dynamic Information Distribution, Lock-in Calculation
Abstract: When are we locked in a path? This is one of the main questions concerning path dependency. Coming from Arthur\'s model of increasing returns and technology adoption (Arthur 1989), this paper addresses the question of when and how a lock-in occurs. To gain a better understanding of the path process, different modifications are made. First, the random selection of two types of adopters is substituted with a random selection of adopters having a Gaussian distributed natural inclination. Second, Arthur\'s model shows only indirect network effects, so direct network effects are added to the model. Furthermore, it is shown that there is an asymptotic lock-in function referring to the technology A and B adopter ratio; this ratio is calculated within the process on the basis of a returning probability to an open state. In the following, the developed model is used to simulate path process without increasing returns, with increasing returns stopping when a lock-in occurs, as well as random drop-outs of increasing returns. One answer that could be drawn out of this new extended model is that there is no lock-in without further stabilizing returns. This and other aspects are used to provide a simplified path-model for empirical research. Finally, its limits are discussed in regard to uncertainty, innovation, and changes in network effect parameters.

Opinion Formation by Informed Agents

Mohammad Afshar and Masoud Asadpour
Journal of Artificial Societies and Social Simulation 13 (4) 5

Kyeywords: Social Networks, Informed Agents, Innovation Diffusion, Bounded Confidence, Opinion Dynamics, Opinion Formation
Abstract: Opinion formation and innovation diffusion have gained lots of attention in the last decade due to its application in social and political science. Control of the diffusion process usually takes place using the most influential people in the society, called opinion leaders or key players. But the opinion leaders can hardly be accessed or hired for spreading the desired opinion or information. This is where informed agents can play a key role. Informed agents are common people, not distinguishable from the other members of the society that act in coordination. In this paper we show that informed agents are able to gradually shift the public opinion toward a desired goal through microscopic interactions. In order to do so they pretend to have an opinion similar to others, but while interacting with them, gradually and intentionally change their opinion toward the desired direction. In this paper a computational model for opinion formation by the informed agents based on the bounded confidence model is proposed. The effects of different parameter settings including population size of the informed agents, their characteristics, and network structure, are investigated. The results show that social and open-minded informed agents are more efficient than selfish or closed-minded agents in control of the public opinion.

Diffusion of Competing Innovations: The Effects of Network Structure on the Provision of Healthcare

Adam G. Dunn and Blanca Gallego
Journal of Artificial Societies and Social Simulation 13 (4) 8

Kyeywords: Innovation Diffusion, Scale-Free Networks, Health Policy, Agent-Based Modelling
Abstract: Medical innovations, in the form of new medication or other clinical practices, evolve and spread through health care systems, impacting on the quality and standards of health care provision, which is demonstrably heterogeneous by geography. Our aim is to investigate the potential for the diffusion of innovation to influence health inequality and overall levels of recommended care. We extend existing diffusion of innovation models to produce agent-based simulations that mimic population-wide adoption of new practices by doctors within a network of influence. Using a computational model of network construction in lieu of empirical data about a network, we simulate the diffusion of competing innovations as they enter and proliferate through a state system comprising 24 geo-political regions, 216 facilities and over 77,000 individuals. Results show that stronger clustering within hospitals or geo-political regions is associated with slower adoption amongst smaller and rural facilities. Results of repeated simulation show how the nature of uptake and competition can contribute to low average levels of recommended care within a system that relies on diffusive adoption. We conclude that an increased disparity in adoption rates is associated with high levels of clustering in the network, and the social phenomena of competitive diffusion of innovation potentially contributes to low levels of recommended care.

Soft Power, World System Dynamics, and Democratization: A Bass Model of Democracy Diffusion 1800-2000

Mikael Sandberg
Journal of Artificial Societies and Social Simulation 14 (1) 4

Kyeywords: Democracy, Bass, Communication, System Dynamics, Power, Diffusion
Abstract: This article uses Polity IV data to probe system dynamics for studies of the global diffusion of democracy from 1800 to 2000. By analogy with the Bass model of diffusion of innovations, as translated into system dynamics by Sterman, the dynamic explanation proposed focuses on transitions to democracy, soft power, and communication rates on a global level. The analysis suggests that the transition from democratic experiences (\'the soft power of democracy\') can be estimated from the systems dynamics simulation of an extended Bass model. Soft power, fueled by the growth in communications worldwide, is today the major force behind the diffusion of democracy. Our findings indicate the applicability of system dynamics simulation tools for the analysis of political change over time in the world system of polities.

Knowledge Diffusion and Innovation: Modelling Complex Entrepreneurial Behaviours by Piergiuseppe Morone and Richard Taylor: A Response to the Review

Piergiuseppe Morone and Richard Taylor
Journal of Artificial Societies and Social Simulation 14 (2) 7

Kyeywords: Knowledge Diffusion, Innovation, Agent-Based Model, Validation
Abstract: In this brief note we reply to César García-Díaz and Diemo Urbig who reviewed our book on Knowledge Diffusion and Innovation (Edward Elgar Publishing: Cheltenham, 2010). We take this opportunity to reaffirm our personal view on several relevant issues, such as the need for a holistic view in economics, the adoption of a pragmatic heuristic approach when dealing with complex socio-economic systems, the relevance of a \'prototype model\' to setting a rigorous conceptual framework and the proposition of a novel way of looking at knowledge and innovation.

An Agent-Based Competitive Product Diffusion Model for the Estimation and Sensitivity Analysis of Social Network Structure and Purchase Time Distribution

Keeheon Lee, Shintae Kim, Chang Ouk Kim and Taeho Park
Journal of Artificial Societies and Social Simulation 16 (1) 3

Kyeywords: Agent-Based Product Diffusion Model, Individual Purchase Time, Social Network Structure, Estimation, Sensitivity Analysis
Abstract: To maximise the possibility of success for a new product and minimise the risk and opportunity cost of a failed product, firms must understand the diffusion dynamics of competing products. The diffusion dynamics of competing products emerge from the aggregation of consumers' decisions. At the individual level, a consumer's decision consists of "which product to buy among the available products" and "when to buy a product". Individual product choices are affected by local and global social interactions among consumers. It would be helpful for firms to be able to determine the characteristics of the relevant social network for their target market and how changes in this social network influence their market shares. In addition, determining the distribution of product purchase times of consumers and how their variation affects market shares are interesting issues for firms. In this study, therefore, we propose an agent-based simulation model that generates the market share paths (market shares over time) of competing products. We apply the model to estimate the social network and purchase time distribution of the Korean netbook market. Our observation is that Korean netbook consumers tend to buy a product without hesitation, and their social network is rather regular but sparse. We also conduct sensitivity analyses with respect to the social network and the purchase time distribution.

Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice

Maxwell Brown
Journal of Artificial Societies and Social Simulation 16 (2) 5

Kyeywords: Electric Vehicle, Diffusion, Mixed Logit, Vehicle Choice, Network Effects
Abstract: This research develops then merges two separate models to simulate electric vehicle diffusion through recreation of the Boston metropolitan statistical area vehicle market place. The first model is a mixed (random parameters) logistic regression applied to data from the US Department of Transportation's 2009 National Household Travel Survey. The second, agent-based model simulates social network interactions through which agents' vehicle choice sets are endogenously determined. Parameters from the first model are applied to the choice sets determined in the second. Results indicate that electric vehicles as a percentages of vehicle stock range from 1% to 22% in the Boston metropolitan statistical area in the year 2030, percentages being highly dependent on scenario specifications. A lower price is the main source of competitive advantage for vehicles but other characteristics, such as vehicle classification and range, are demonstrated to influence consumer choice. Government financial incentive availability leads to greater market shares in the beginning years and helps to spread diffusion in later years due to an increased base of initial adopters. Although seen as a potential hindrance to EV diffusion, battery cost scenarios have relatively small impacts on EV diffusion in comparison to policy, range, miles per gallon (MPG), and vehicle miles travelled (VMT) as a percentage of range assumptions. Pessimistic range assumptions decrease overall PHEV and BEV percentages of vehicle stock by 50% and 30%, respectively, relative to the EPA-estimated range scenarios. Fuel cost scenarios do not considerably alter estimated BEV and PHEV stock but increase the ratio of car stock to light truck stock in the internal combustion engine (ICE) vehicle spectrum. Specifically, cars are estimated at 55% of ICE vehicle stock in the default fuel price scenario but increase to 62% of ICE vehicle stock in the high world oil price scenario, with LTs covering the appropriate differences.

Pricing and Timing Strategies for New Product Using Agent-Based Simulation of Behavioural Consumers

Keeheon Lee, Hoyeop Lee and Chang Ouk Kim
Journal of Artificial Societies and Social Simulation 17 (2) 1

Kyeywords: Product Diffusion, Pricing and Time Strategies, Korean Mobile Phone Market, Sensitivity Analysis
Abstract: In this study, we are interested in the problem of determining the pricing and timing strategies of a new product by developing an agent-based product diffusion simulation. In the proposed simulation model, agents imitate behavioural consumers, who are reference dependent and risk averse in the evaluation of new products and whose interactions create word-of-mouth regarding new products. Pricing and timing strategies involve the timing of a new product release, the timing of providing a discount on a new product, and the relative rates of discounts. We conduct two experiments in this study. In both experiments, we consider the urban young person segment in the mobile phone market in Korea, in which three major new products - two smartphones and one convergence product - compete with one another. The first experiment is sensitivity analysis on the product life cycle and social influence. The objective is to observe how consumer agents behave as the product life cycle and the degree of sensitivity on social influence change. The second experiment is sensitivity analysis on time-to-market, time-to-discount, and amount-to-discount. The marketing strategy that maximises the sales volume (or revenue) of the new convergence product is sought from the sensitivity analysis. Based on the result, we provide pricing and timing implications for firms pursuing sales volumes (or revenue) increase.

An Agent-Based Model of Urgent Diffusion in Social Media

William Rand, Jeffrey Herrmann, Brandon Schein and Neža Vodopivec
Journal of Artificial Societies and Social Simulation 18 (2) 1

Kyeywords: Urgent Diffusion, Diffusion of Information, News, Social Networks, Twitter
Abstract: During a crisis, understanding the diffusion of information throughout a population will provide insights into how quickly the population will react to the information, which can help those who need to respond to the event. The advent of social media has resulted in this information spreading quicker then ever before, and in qualitatively different ways, since people no longer need to be in face-to-face contact or even know each other to pass on information in an crisis situation. Social media also provides a wealth of data about this information diffusion since much of the communication happening within this platform is publicly viewable. This data trove provides researchers with unique information that can be examined and modeled in order to understand urgent diffusion. A robust model of urgent diffusion on social media would be useful to any stakeholders who are interested in responding to a crisis situation. In this paper, we present two models, grounded in social theory, that provide insight into urgent diffusion dynamics on social networks using agent-based modeling. We then explore data collected from Twitter during four major urgent diffusion events including: (1) the capture of Osama Bin Laden, (2) Hurricane Irene, (3) Hurricane Sandy, and (4) Election Night 2012. We illustrate the diffusion of information during these events using network visualization techniques, showing that there appear to be differences. After that, we fit the agent-based models to the observed empirical data. The results show that the models fit qualitatively similarly, but the diffusion patterns of these events are indeed quite different from each other.

Intervention Strategies and the Diffusion of Collective Behavior

Hai-hua Hu, Jun Lin and Wen-tian Cui
Journal of Artificial Societies and Social Simulation 18 (3) 16

Kyeywords: Intervention Strategy, Diffusion of Collective Behavior, Social Network, Agent-Based Modeling
Abstract: This paper examines the intervention strategies for the diffusion of collective behavior, such as promoting innovation adoption and repressing a strike. An intervention strategy refers to controlling the behaviors of a small number of individuals in terms of their social or personal attributes, including connectivity (i.e., the number of social ties one holds), motivation (i.e., an individual’s intrinsic cost–benefit judgment on behavior change), and sensitivity (i.e., the degree to which one follows others). Extensive agent-based simulations demonstrate that the optimal strategy fundamentally depends on the goal and time of intervention. Moreover, the nature of the social network (determined by homophily type and level) moderates the effectiveness of a strategy. These results have substantial implications for the design and evaluation of intervention programs.

An Empirically Grounded Model of Green Electricity Adoption in Germany: Calibration, Validation and Insights into Patterns of Diffusion

Friedrich Krebs
Journal of Artificial Societies and Social Simulation 20 (2) 10

Kyeywords: Green Electricity, Innovation Diffusion, Spatially Explicit Agent-Based Model, Empirical Calibration and Validation
Abstract: Spatially explicit agent-based models (ABM) of innovation diffusion have experienced growing attention over the last few years. The ABM presented in this paper investigates the adoption of green electricity tariffs by German households. The model represents empirically characterised household types as agent types which differ in their decision preferences regarding green electricity and other psychological properties. Agent populations are initialised based on spatially explicit socio demographic data describing the sociological lifestyles found in Germany. For model calibration and validation we use historical data on the German green electricity market including a rich dataset of spatially explicit customer data of one of the major providers of green electricity. In order to assess the similarity of the simulation results to historical observations we introduce two validation measures which capture different aspects of the green electricity diffusion. One measure is based on the residuals of spatially-aggregated time series of model indicators and the other measure considers a temporally aggregated but spatially disaggregated indicator of spatial spread. Finally, we demonstrate the descriptive richness of the model by investigating simulation outputs of the calibrated model in more detail. In particular, the results provide insights into the dynamics of the spatial and lifestyle heterogeneity “underneath” the diffusion curve of green electricity in Germany.

Product Diffusion Using Advance Selling Strategies: An Online Social Network Perspective

Peng Shao and Ping Hu
Journal of Artificial Societies and Social Simulation 20 (2) 2

Kyeywords: Advance Selling, Product Diffusion, Social Network, Complex Network
Abstract: This study analyzes the diffusion of two product types using an advance selling strategy from a social network perspective. We extended the susceptible-infected-removed (SIR) model by adding a buyer component (SIRB) to the model and conducted an in-depth analysis of transmission probability and purchase probability when using an advance selling strategy. Agent-based simulation indicates that cost reduction and promotional effort have positive effects on profits, while lead time negatively affects them. Statistical analyses indicate that lead time has a U-shaped relationship with profits for non-durable products, but an inverted U-shaped relationship with those for durable products. For both products types, promotional effort has an inverted U-shaped relationship with profits under the condition of low-quality products and an inverted U-shaped relationship in the case of high-quality products. The reasons underlying these results are discussed, followed by implications for firms adopting advance selling strategies.

Responsiveness of Mining Community Acceptance Model to Key Parameter Changes

Mark Kofi Boateng and Kwame Awuah-Offei
Journal of Artificial Societies and Social Simulation 20 (3) 4

Kyeywords: Mining Community, Agent-Based Modeling, Diffusion, Sensitivity Analysis, Mining
Abstract: The mining industry has difficulties predicting changes in the level of community acceptance of its projects over time. These changes are due to changes in the society and individual perceptions around these mines as a result of the mines’ environmental and social impacts. Agent-based modeling can be used to facilitate better understanding of how community acceptance changes with changing mine environmental impacts. This work investigates the sensitivity of an agent-based model (ABM) for predicting changes in community acceptance of a mining project due to information diffusion to key input parameters. Specifically, this study investigates the responsiveness of the ABM to average degree (total number of friends) of the social network, close neighbor ratio (a measure of homophily in the social network) and number of early adopters (“innovators”). A two-level full factorial experiment was used to investigate the sensitivity of the model to these parameters. The primary (main), secondary and tertiary effects of each parameter were estimated to assess the model’s sensitivity. The results show that the model is more responsive to close neighbor ratio and number of early adopters than average degree. Consequently, uncertainty surrounding the inferences drawn from simulation experiments using the agent-based model will be minimized by obtaining more reliable estimates of close neighbor ratio and number of early adopters. While it is possible to reliably estimate the level of early adopters from the literature, the degree of homophily (close neighbor ratio) has to be estimated from surveys that can be expensive and unreliable. Further, work is required to find economic ways to document relevant degrees of homophily in social networks in mining communities.

Networks, Percolation, and Consumer Demand

Paolo Zeppini and Koen Frenken
Journal of Artificial Societies and Social Simulation 21 (3) 1

Kyeywords: Clustering, Diffusion, Efficiency, Phase Transition, Small-World Network, Welfare
Abstract: Understanding diffusion processes is key to market strategies as well as innovation and sustainability policies. In promoting new products and technologies, firms and governments need to understand the conditions favouring successful spread of these products. We propose a generic diffusion model based on percolation theory. Our reference is a new product diffusion in a social network through word-of-mouth. Given that consumers differ in their reservation prices, a critical price exists that defines a phase transition from a no-diffusion to a diffusion regime. As consumer surplus is maximised just below a product’s critical price, one can systematically compare the economic efficiency of network structures by investigating their critical price. Networks with low clustering were the most efficient, because clustering leads to redundant information flows hampering effective product diffusion. We further showed that the more equal a society, the more efficient the diffusion process.

An Agent-Based Model of Residential Energy Efficiency Adoption

Magnus Moglia, Aneta Podkalicka and James McGregor
Journal of Artificial Societies and Social Simulation 21 (3) 3

Kyeywords: Energy Efficiency, Policy Assessment, Innovation Diffusion, Solar Hot Water, Consumat, Ex-Ante
Abstract: This paper reports on an Agent-Based Model. The purpose of developing this model is to describe ‘the uptake of low carbon and energy efficient technologies and practices by households and under different interventions’. There is a particular focus on modelling non-financial incentives as well as the influence of social networks as well as the decision making by multiple types of agents in interaction, i.e. recommending agents and sales agents, not just households. The decision making model for householder agents is inspired by the Consumat approach, as well as some of those recently applied to electric vehicles. A feature that differentiates this model is that it also represents information agents that provide recommendations and sales agents that proactively sell energy efficient products. By applying the model to a number of scenarios with policies aimed at increasing the adoption of solar hot water systems, a range of questions are explored, including whether it is more effective to incentivise sales agents to promote solar hot water systems, or whether it is more effective to provide a subsidy directly to households; or in fact whether it is better to work with plumbers so that they can promote these systems. The resultant model should be viewed as a conceptual structure with a theoretical and empirical grounding, but which requires further data collection for rigorous analysis of policy options.

Identifying Mechanisms Underlying Peer Effects on Multiplex Networks

Hang Xiong, Diane Payne and Stephen Kinsella
Journal of Artificial Societies and Social Simulation 21 (4) 6

Kyeywords: Peer Effects, Social Networks, Diffusion of Innovation, High-Value Crop
Abstract: We separately identify two mechanisms underlying peer effects in farm households' adoption of a new crop. A farmer can follow his peers to adopt a new crop because he learns knowledge about the new crop from them (social learning) and because he wants to avoid the damage caused by the practice conflicting with theirs (externalities). Using an agent-based model, we simulate the two mechanisms on a multiplex network consisting of two types of social relationships. The simulation model is estimated using detailed data of social networks, adoption and relevant socio-economic characteristics from 10 villages in China. We find that social learning -- in this case, the sharing of experiential resources -- among family members and production externalities between contiguous land plots both significantly influence farmers' adoption. Furthermore, sharing of experiential resources plays a significant role in the entire diffusion process and dominates the early phase, whereas externalities only matter in the late phase. This study shows the roles peer effects play in shaping diffusion can occur through different mechanisms and can vary as the diffusion proceeds. The work also suggests that agent-based models can help disentangle the role of social interactions in promoting or hindering diffusion.

Community-Based Adoption and Diffusion of Micro-Grids: Analysis of the Italian Case with Agent-Based Model

Francesco Pasimeni
Journal of Artificial Societies and Social Simulation 22 (1) 11

Kyeywords: Micro-Grids, Agent-Based Model, Innovation Diffusion, Energy Transition
Abstract: The electricity generation and distribution system in many developed economies is based primarily on the centralised grid. However, there is a need to shift from this traditional system to a newly more decentralised electricity system. This paper explores possible scenarios of adoption and diffusion of Micro-Grids (MGs) in Italy. An agent-based model is formulated to simulate the diffusion process as function of regional factors, subsidies and people's attitude. It assumes that MGs are purchased directly by communities of neighbours, which benefit from cost sharing. Results show high dependence of the diffusion process on regional factors: electricity demand, renewable potential and population. The model confirms that subsidies boost diffusion, mainly when they are regional-based rather than national-based. Higher green attitude accelerates diffusion and reduces environmental impact of the electricity system.

Coevolutionary Characteristics of Knowledge Diffusion and Knowledge Network Structures: A GA-ABM Model

Junhyok Jang, Xiaofeng Ju, Unsok Ryu and Hyonchol Om
Journal of Artificial Societies and Social Simulation 22 (3) 3

Kyeywords: Knowledge Diffusion, Knowledge Network, Coevolutionary, Genetic Algorithm, Agent-Based Modeling
Abstract: The co-evolutionary dynamics of knowledge diffusion and network structure in knowledge management is a recent research trend in the field of complex networks. The aim of this study is to improve the knowledge diffusion performance of knowledge networks including personnel, innovative organizations and companies. In order to study the co-evolutionary dynamics of knowledge diffusion and network structure, we developed a genetic algorithm-agent based model (GA-ABM) by combining a genetic algorithm (GA) and an agent-based model (ABM). Our simulations show that our GA-ABM improved the average knowledge stock and knowledge growth rate of the whole network, compared with several other models. In addition, it was shown that the topological structure of the optimal network obtained by GA-ABM has the property of a random network. Finally, we found that the clustering coefficients of agents are not significant to improve knowledge diffusion performance.

Modelling Contingent Technology Adoption in Farming Irrigation Communities

Antoni Perello-Moragues, Pablo Noriega and Manel Poch
Journal of Artificial Societies and Social Simulation 22 (4) 1

Kyeywords: Agent-Based Modeling, Innovation Diffusion, Policy-Making, Irrigation Agriculture, Socio-Hydrology
Abstract: Of all the uses of water, agriculture is the one that requires the greatest proportion of resources worldwide. Consequently, it is a salient subject for environmental policy-making, and adoption of modern irrigation systems is a key means to improve water use efficiency. In this paper we present an agent-based model of the adoption process —known as "modernisation"— of a community constituted by farmer agents. The phenomenon is approached as a contingent innovation adoption: a first stage to reach a collective agreement followed by an individual adoption decision. The model is based on historical data from two Spanish irrigation communities during the period 1975-2010. Results suggest that individual profits and farm extension (as proxy of social influence) are suitable assumptions when modelling the modernisation of communities in regions where agriculture is strongly market-oriented and water is scarce. These encouraging results point towards the interest of more sophisticated socio-cognitive modelling within a more realistic socio-hydrologic context.

Agent-Based Modelling of Charging Behaviour of Electric Vehicle Drivers

Mart van der Kam, Annemijn Peters, Wilfried van Sark and Floor Alkemade
Journal of Artificial Societies and Social Simulation 22 (4) 7

Kyeywords: Electric Vehicles, Intermittent Renewables, Smart Charging, Environmental Self-Identity, Range Anxiety, Agent-Based Model
Abstract: The combination of electric vehicles (EVs) and intermittent renewable energy sources has received increasing attention over the last few years. Not only does charging electric vehicles with renewable energy realize their true potential as a clean mode of transport, charging electric vehicles at times of peaks in renewable energy production can help large scale integration of renewable energy in the existing energy infrastructure. We present an agent-based model that investigates the potential contribution of this combination. More specifically, we investigate the potential effects of different kinds of policy interventions on aggregate EV charging patterns. The policy interventions include financial incentives, automated smart charging, information campaigns and social charging. We investigate how well the resulting charging patterns are aligned with renewable energy production and how much they affect user satisfaction of EV drivers. Where possible, we integrate empirical data in our model, to ensure realistic scenarios. We use recent theory from environmental psychology to determine agent behaviour, contrary to earlier simulation models, which have focused only on technical and financial considerations. Based on our simulation results, we articulate some policy recommendations. Furthermore, we point to future research directions for environmental psychology scholars and modelers who want to use theory to inform simulation models of energy systems.

A Dynamic Computational Model of Social Stigma

Myong-Hun Chang and Joseph Harrington
Journal of Artificial Societies and Social Simulation 23 (2) 1

Kyeywords: Stigma, Diffusion, Conformity, Compassion, Social Network
Abstract: The dynamics of social stigma are explored in the context of diffusion models. Our focus is on exploring the dynamic process through which the behavior of individuals and the interpersonal relationships among them influence the macro-social attitude towards the stigma. We find that a norm of tolerance is best promoted when the population comprises both those whose conduct is driven by compassion for the stigmatized and those whose focus is on conforming with others in their social networks. A second finding is that less insular social networks encourage de-stigmatization when most people are compassionate, but it is instead more insularity that promotes tolerance when society is dominated by conformity.

Reflexivity in a Diffusion of Innovations Model

Carlos Córdoba and César García-Díaz
Journal of Artificial Societies and Social Simulation 23 (3) 9

Kyeywords: Reflexivity, Diffusion of Innovations, Second-Order Emergence, Global Network Externalities
Abstract: Reflexive phenomena are usually understood in the social sciences as processes that affect themselves recursively. This stems from the mutual altering relationship between participants and the social process they belong to: participants can change the course of the process with their actions and a new state during the evolution of the process can lead to a change in its participants' behavior. This article proposes an agent-based model of diffusion of innovations in a social network to study reflexivity. In this model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood. Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect. The contribution of this work lies in the inclusion of evolving cognitive distinctions as agents decide product adoption in diffusion processes.

Halting SARS-CoV-2 by Targeting High-Contact Individuals

Gianluca Manzo and Arnout van de Rijt
Journal of Artificial Societies and Social Simulation 23 (4) 10

Kyeywords: Agent-Based Computational Models, Complex Social Networks, Virus Diffusion, Immunization Strategies, Epidemiological Models
Abstract: Network scientists have proposed that infectious diseases involving person-to-person transmission could be effectively halted by interventions targeting a minority of highly connected individuals. Could this strategy be effective in combating a virus partly transmitted in close-range contact, as many believe SARS-CoV-2 to be? Effectiveness critically depends on high between-person variability in the number of close-range contacts. We analyzed population survey data showing that the distribution of close-range contacts across individuals is indeed characterized by a small proportion of individuals reporting very high frequency contacts. Strikingly, we found that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs. We simulated a population embedded in a network with empirically observed contact frequencies. Simulations showed that targeting hubs robustly improves containment.

Seed Selection Strategies for Information Diffusion in Social Networks: An Agent-Based Model Applied to Rural Zambia

Beatrice Nöldeke, Etti Winter and Ulrike Grote
Journal of Artificial Societies and Social Simulation 23 (4) 9

Kyeywords: Information Diffusion, Social Networks, Agent-Based Modelling, Seeding, Zambia
Abstract: The successful adoption of innovations depends on the provision of adequate information to farmers. In rural areas of developing countries, farmers usually rely on their social networks as an information source. Hence, policy-makers and program-implementers can benefit from social diffusion processes to effectively disseminate information. This study aims to identify the set of farmers who initially obtain information (‘seeds’) that optimises diffusion through the network. It systematically evaluates different criteria for seed selection, number of seeds, and their interaction effects. An empirical Agent-Based Model adjusted to a case study in rural Zambia was applied to predict diffusion outcomes for varying seed sets ex ante. Simulations revealed that informing farmers with the most connections leads to highest diffusion speed and reach. Also targeting village heads and farmers with high betweenness centrality, who function as bridges connecting different parts of the network, enhances diffusion. An increased number of seeds improves reach, but the marginal effects of additional seeds decline. Interdependencies between seed set size and selection criteria highlight the importance of considering both seed selection criteria and seed set size for optimising seeding strategies to enhance information diffusion.

Structural Effects of Agent Heterogeneity in Agent-Based Models: Lessons from the Social Spread of COVID-19

D. Cale Reeves, Nicholas Willems, Vivek Shastry and Varun Rai
Journal of Artificial Societies and Social Simulation 25 (3) 3

Kyeywords: Agent-Based Model, Diffusion Model, Empirical Data-Driven Model, Heterogeneous Population, Model Performance, COVID-19
Abstract: Modeling human behavior in the context of social systems in which we are embedded realistically requires capturing the underlying heterogeneity in human populations. However, trade-offs associated with different approaches to introducing heterogeneity could either enhance or obfuscate our understanding of outcomes and the processes by which they are generated. Thus, the question arises: how to incorporate heterogeneity when modeling human behavior as part of population-scale phenomena such that greater understanding is obtained? We use an agent-based model to compare techniques of introducing heterogeneity at initialization or generated during the model’s runtime. We show that initializations with unstructured heterogeneity can interfere with a structural understanding of emergent processes, especially when structural heterogeneity might be a key part of driving how behavioral responses dynamically shape emergence in the system. We find that incorporating empirical population heterogeneity – even in a limited sense – can substantially contribute to improved understanding of how the system under study works.

Calibrating Agent-Based Models of Innovation Diffusion with Gradients

Florian Kotthoff and Thomas Hamacher
Journal of Artificial Societies and Social Simulation 25 (3) 4

Kyeywords: Agent-Based Modeling, Multi-Agent Simulation, Innovation Diffusion, Adoption Model, Decision Making, Calibration
Abstract: Consumer behavior and the decision to adopt an innovation are governed by various motives, which models find difficult to represent. A promising way to introduce the required complexity into modeling approaches is to simulate all consumers individually within an agent-based model (ABM). However, ABMs are complex and introduce new challenges. Especially the calibration of empirical ABMs was identified as a key difficulty in many works. In this work, a general ABM for simulating the Diffusion of Innovations is described. The ABM is differentiable and can employ gradient-based calibration methods, enabling the simultaneous calibration of large numbers of free parameters in large-scale models. The ABM and calibration method are tested by fitting a simulation with 25 free parameters to the large data set of privately owned photovoltaic systems in Germany, where the model achieves a coefficient of determination of R2 ≃ 0.7.