Agent-based Computational Model Research Articles

Reputation systems and recruitment in online labor markets: insights from an agent-based model

Online labor markets—freelance marketplaces, where digital labor is distributed via a web-based platform—commonly use reputation systems to overcome uncertainties in the hiring process, that can arise from a lack of objective information about employees’ abilities. Research shows, however, that reputation systems tend to create winner-takes-all dynamics, in which differences in candidates’ reputations become disconnected from differences in their objective abilities. In this paper, we use an empirically validated agent-based computational model to investigate the extent to which reputation systems can create segmented hiring patterns that are biased toward freelancers with good reputation. We explore how jobs and earnings become distributed on a stylized platform, under different contextual conditions of information asymmetry. Our results suggest that information asymmetry influences the extent to which reputation systems may lead to inequality between freelancers, but contrary to our expectations, lower levels of information asymmetry can facilitate higher inequality in outcomes.

Cognitive attunement in the face of organizational plasticity

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

Potencial criativo e padrões de migração

O presente trabalho formula um modelo computacional baseado em agentes visando compreender o comportamento migratório da classe criativa e os padrões de aglomeração urbana a ele associados, assim como a relação de tais padrões com o potencial criativo. Na formulação do modelo, foram utilizados conceitos de geografia evolutiva econômica, teoria de redes e a literatura de economia criativa. O modelo simula o aprendizado, interação e migração dos agentes, considerando diversidade étnica, política e sexual, assim como as amenidades urbanas e oportunidades de emprego como fatores atratores da classe criativa.Como resultado, observa-se uma relação exponencial entre o potencial criativo e população de uma localidade, sugerindo uma vantagem relativa e absoluta dos grandes centros no que tange ao potencial para inovar. Considerando as variáveis que interferem na velocidade de crescimento do potencial criativo, a quantidade de conhecimento trocado entre os agentes parece exercer um efeito potencializador superior ao número de interações entre agentes.

Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling.

In January 2020, a novel betacoronavirus (family Coronaviridae), named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the etiological agent of a cluster of pneumonia cases occurring in Wuhan City, Hubei Province, China1,2. The disease arising from SARS-CoV-2 infection, coronavirus disease 2019 (COVID-19), subsequently spread rapidly causing a worldwide pandemic. Here we examine the added value of near real-time genome sequencing of SARS-CoV-2 in a subpopulation of infected patients during the first 10 weeks of COVID-19 containment in Australia and compare findings from genomic surveillance with predictions of a computational agent-based model (ABM). Using the Australian census data, the ABM generates over 24 million software agents representing the population of Australia, each with demographic attributes of an anonymous individual. It then simulates transmission of the disease over time, spreading from specific infection sources, using contact rates of individuals within different social contexts. We report that the prospective sequencing of SARS-CoV-2 clarified the probable source of infection in cases where epidemiological links could not be determined, significantly decreased the proportion of COVID-19 cases with contentious links, documented genomically similar cases associated with concurrent transmission in several institutions and identified previously unsuspected links. Only a quarter of sequenced cases appeared to be locally acquired and were concordant with predictions from the ABM. These high-resolution genomic data are crucial to track cases with locally acquired COVID-19 and for timely recognition of independent importations once border restrictions are lifted and trade and travel resume.

Inequalities, chance and success in sport competitions: Simulations vs empirical data

We present a new way of estimation of the role of chance in achieving success, by comparing the empirical data from 100-m dash competitions (one of the sports disciplines with the most stringent controls of external randomness), with the results of an agent-based computer model, which assumes that success depends jointly on the intrinsic talent of the agent and on unpredictable luck. We find a small, but non-zero contribution of random luck to the performance of the best sprinters, which may serve as a lower bound for the randomness role in other, less stringently controlled competitive domains. Additionally we discuss the perception of the payoff differences among the top participants, and the role of random luck in the resulting inequality.

Improving Representation of Decision Rules in LUCC-ABM: An Example with an Elicitation of Farmers’ Decision Making for Landscape Restoration in Central Malawi

Restoring interlocking forest-agricultural landscapes—forest-agricscapes—to sustainably supply ecosystem services for socio-ecological well-being is one of Malawi’s priorities. Engaging local farmers is crucial in implementing restoration schemes. While farmers’ land-use decisions shape land-use/cover and changes (LUCC) and ecological conditions, why and how they decide to embrace restoration activities is poorly understood and neglected in forest-agricscape restoration. We analyze the nature of farmers’ restoration decisions, both individually and collectively, in Central Malawi using a mixed-method analysis. We characterize, qualitatively and quantitatively, the underlying contextual rationales, motives, benefits, and incentives. Identified decision-making rules reflect diverse and nuanced goal frames of relative importance that are featured in various combinations. We categorize the decision-making rules as: problem-solving oriented, resource/material-constrained, benefits-oriented, incentive-based, peers/leaders-influenced, knowledge/skill-dependent, altruistic-oriented, rules/norms-constrained, economic capacity-dependent, awareness-dependent, and risk averse-oriented. We link them with the corresponding vegetation- and non-vegetation-based restoration practices to depict the overall decision-making processes. Findings advance the representation of farmers’ decision rules and behavioral responses in computational agent-based modeling (ABM), through the decomposition of empirical data. The approach used can inform other modeling works attempting to better capture social actors’ decision rules. Such LUCC-ABMs are valuable for exploring spatially explicit outcomes of restoration investments by modeling such decision-making processes and policy scenarios.

Misfolded protein propagation in an integrated computational model of structural network and LRRK2 gene expression.

It has been widely accepted that Parkinson's disease (PD) is triggered and shaped by propagation of misfolded α-synuclein. Converging neurophysiological evidence suggests that leucine-rich repeat kinase 2 (LRRK2) is involved in membrane transport of PD pathogenesis. This study proposed an agent-based computational model by integrating structural connections and gene expression to investigate whether LRRK2 would affect the PD pathology propagation in central nervous system. Gene expression profiles from the Allen Human Brain Atlas (AHBA) and multimodal brain MRI images from Parkinson's Progression Markers Initiative (PPMI) and Human Connectome Project (HCP) were employed for the model construction. The model results exhibit the involvement of LRRK2 gene expression remarkably elevated model fitting (r = 0.73) compared with the traditional susceptible-infected-removed (S-I-R) model (r=0.60). Specifically, our model revealed that LRRK2 is more likely to modulate pathology secretion out of neurons, rather than spreading into neurons. The findings support the theory of LRRK2 gene expression modulating cell-to-cell propagation of misfolded proteins. As a result, the proposed model would bring new insights of understanding PD mechanism in terms of misfolded α-synuclein propagation.

Agent-based models and methodological individualism: are they fundamentally linked?

Cet article étudie la littérature sur le bien-fondé du lien entre les modèles à base d’agents artificiels et l’individualisme méthodologique. L’argument défendu est que ces analyses reposent sur une définition spécifique de l’individualisme méthodologique rendant ainsi les conclusions dépendantes de la définition retenue. L’article propose alors de considérer les modèles à base d’agents artificiels et l’individualisme méthodologique comme des « instruments génériques », à savoir des dispositifs ayant des propriétés transversales à des problèmes explicatifs, à des domaines d’étude et à des disciplines hétérogènes. De ce point de vue, les modèles à base d’agents artificiels et l’individualisme méthodologique semblent partager des principes élémentaires qui sont indépendants des entités et des niveaux d’analyse propres à un objet d’étude particulier. En ce sens, cet article soutient finalement que les modèles à base d’agents artificiels et l’individualisme méthodologique sont intrinsèquement liés.

Repetitive floods intensify outmigration and climate gentrification in coastal cities

Recent floods in America, Europe, Asia and Africa reminded societies across the world of the need to revisit their climate adaptation strategies. Rapid urbanization coinciding with a growing frequency and intensity of floods requires transformative actions in cities worldwide. While abandoning flood prone areas is sometimes discussed as a public climate adaptation option, little attention is paid to studying cumulative impacts of outmigration as an individual choice. To explore the aggregated consequences of households’ outmigration decisions in response to increasing flood hazards, we employ a computational agent-based model grounded in empirical heuristics of buyers’ and sellers’ behaviour in a flood-prone housing market. Our results suggest that pure market-driven processes can cause shifts in demographics in climate-sensitive hotspots placing low-income households further at risk. They get trapped in hazard zones, even when individual risk perceptions and behavioural location preferences are independent of income, suggesting increasing climate gentrification as an outcome of market sorting.

Interactions between worker ants may influence the growth of ant cemeteries

When an ant dies within a nest, a worker ant carries its corpse away from the nest and drops it onto a pile known as an ant cemetery. These ant cemeteries form cluster patterns, and the dynamics of the corpse piles have been studied experimentally. The aim of the present study was to investigate how sensitivity to the presence of nest-mates would influence the corpse-carrying behaviour of ants, and how this would impact the dynamics of corpse pile clustering. This was achieved by developing an agent-based computational model in which simulated ‘ants’ (the agents) carry and drop ‘corpses’, resulting in the growth of the corpse pile. In the model, the probability of an ant dropping a corpse was tuned according to the presence or absence of nest-mates. The pile dynamics of the resulting model showed a partial match with the time series evolution of corpse piles observed with real ants in previous experimental studies. Although the switch of probabilities is a thought experiment, our results suggest that the corpse-carrying behaviour of worker ants might be influenced by interactions with their nest-mates because there is evidence that ant behaviour can be influenced by encounter rates.

EVOLUTION OF THE FIRM SIZE DISTRIBUTION IN TURKEY

Evolution in economics has been treated mainly as a theoretical journey. The methodological difficulties reside in strict assumptions in order to maintain a tractable model for firm dynamics. Agent-Based Computational modeling could be an important breakthrough for evolutionary applications. However, these models rely on deep interpretations of the complete data. In this study, in depth analysis of Turkish firm level data has been presented with an evolutionary point of view using the method of density estimations. The comprehensiveness of the data is unique to the literature. For modeling purposes, Turkish firms could be seen as representing their birth era. The stagnant nature of firms could also be considered as linked to the lack of managerial delegation in underdeveloped economies.

Sociomathematical Norms for Integrating Coding and Modeling with Elementary Science: A Dialogical Approach

In recent years, the field of education has challenged researchers and practitioners to incorporate computing as an essential focus of K-12 STEM education. Integrating computing within K-12 STEM supports learners of all ages in codeveloping and using computational thinking in existing curricular contexts alongside practices essential for developing mathematical and scientific expertise. In this paper, we present findings from a design-based, microgenetic study in which an agent-based programming and computational modeling platform—ViMAP—was integrated with existing elementary science and math curricula through lessons co-designed and taught by the classroom teacher across a period of seven months. We present a dialogical re-positioning of coding, where disciplinarily grounded meanings of code emerge through the construction of computational utterances––i.e., computer models as well as complementary conversations and physical models that serve as mathematical and scientific explanations––through the use of socio-mathematical norms.

From cell to multi-crypt: Agent-based models of the human colon suggests novel processes of Field cancerisation

Colorectal cancer (CRC) is a major cause of cancer mortality. It is known that loss of APC gene function through mutation is followed by the expansion of a field of mutated tissue, but the mechanisms behind this expansion are poorly understood.This study aimed to examine the processes involved in field expansion using two agent-based computational models: a cell-scale model allowing mapping of Apc-mutated cell expansion in small multcrypt arrays, and a tissue-scale model allowing simulation of the entire colon over oncologically relevant timescales.The cell scale model predicts that mutated cells spread through the flat mucosa of the simulated tissue without invading neighbouring crypts - a process not previously hypothesised in the literature. The crypt-scale model’s predictions of field sizes correspond to those estimated in the literature from in vivo studies. Our dual-scale modelling approach renders the spatial and temporal scales at which field cancerisation processes occur in vivo accessible to exploration by simulation for the first time.

Collective and contractile filament motions in the myosin motility assay.

Cells require mechanical forces for their physiological functions. The forces are generated mainly from molecular interactions between actin filaments, cross-linking proteins, and myosin motors in the actin cytoskeleton. To better understand the molecular interactions, many studies employed myosin motility assays with actin filaments propelled by myosin heads fixed on a surface. Various interesting behaviors of actin filaments have been observed in the motility assay experiments. Despite the popularity of the motility assays, there were only a few computational models designed for simulating the motility assay systems. Most of the previous models have limitations which precluded full understanding of molecular origins for behaviors of actin filaments. In this study, we used an agent-based computational model based on Brownian dynamics for simulating the motility assay system. Our model rigorously describes the mechanics, dynamics, and interactions of actin filaments, cross-linking proteins, and molecular motors. Using the model, we first investigated how properties of actin filaments and motors affect gliding motions of actin filaments without volume-exclusion effects as a base study. We found that actin filaments can continuously glide at relative fast speed only when they are sufficiently longer than the average spacing between neighboring motors and that the gliding speed of F-actins shows a biphasic dependence on processivity of motors. Then, we showed that volume-exclusion effects between actin filaments can induce diverse collective movements and alignment of actin filaments, thus creating thick bundles and ring-like structures in the absence of cross-linking proteins. Lastly, we demonstrated that cross-linking proteins can lead to distinct contractile behaviors of actin networks depending on the density and kinetics of the cross-linking proteins. Results from our study show the ability of our model to simulate the motility assay system under various conditions and provide insights into understanding of different behaviors of actin filaments.

Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

A theory of the evolution of technology: Technological parasitism and the implications for innovation magement

How do technologies develop? This study proposes the theory of technological parasitism that is based on the idea that parasite-host relationships between technologies and technologies with a high number of technological parasites have an accelerated evolution driven by long-run mutualistic symbioses, providing the basis for extensive macroevolution and adaptive behavior of systems of interactive technologies in markets. This theory may be useful for bringing a new perspective to explain and generalize the evolution of technology directed to sustain competitive advantage of firms and nations. In particular, technological parasitism explains the relationship of mutualistic symbiosis between a host (or master) technology and inter-related technologies to satisfy needs and/or to solve consequential problems of socioeconomic subjects over time. To explore the potential of adopting a theory of technological parasitism and to predict which technologies are likeliest to evolve rapidly, this study implements a theoretical test based on a computational agent-based model developed from these concepts and a empirical test based on historical data on the evolution of four example technologies (aircraft, tractor, locomotive and bicycle technology). Computational and empirical evidence are broadly consistent with the theoretical expectation that host (or master) technologies with many associated parasitic technologies advance rapidly, whereas master technologies with fewer parasitic technologies improve slowly. The proposed theory has a demonstrated capacity to explain relationships between technologies in complex systems that clarify the driving forces of the evolution of technology in markets. The finding of this study could aid management of firms and innovation strategy of nations to implement best practices of product/process design and development for supporting R&D of technologies that are likely to evolve rapidly, sustaining and safeguarding competitive advantage. Overall, then, this study, for the first time to our knowledge, begins the process of clarifying and generalizing, as far as possible, the role of long-run coevolution between host and parasitic technologies in complex systems, suggesting fruitful implications for innovation management. markets. This conceptual scheme here is especially relevant in Schumpeterian markets with innovation-based competition to explain a major source of technological evolution and success, the parasite-host relationships between technologies. Finally, this theory here suggests a new direction for the development of more sophisticated concepts and theoretical frameworks to explain technological and industrial change in economic systems.

Signs of Heritage—An Agent-Based Model of the Dynamics of Heritage Categories

In recent times, heritage has become a key focus of socio-economic concern due to its connections to history, nationalism, and, especially, tourism. It has been highlighted as having major consequences for the sociocultural engagement of local populations. The notion that tourism transforms local heritage through the categorization of local realities has become particularly widespread. However, the specific mechanism through which these transformations occur has remained unformalized in cultural heritage studies. This has resulted in a lack of computational approaches to the issue of heritage categorization, which can connect with other issues of tourism promotion and management. In this article, we conceptualize the cultural dynamics of heritage, such as changes in the categories used to signify heritage, and design and analyze a dedicated computational agent-based model of heritage engagement. Here, we seek to explore the role of communicative strategies, as they relate to social groups, as well as the specific topologies of interaction networks. We attempt to understand what role mediation, such as that produced by online platforms, can play in producing different levels of consensus between tourists. The results suggest that topology plays the greatest role in generating consensus in the evolution of heritage meaning, with different patterns emerging according to the communicative strategies employed. The results have implications for the future direction of the study of heritage and heritage modeling, placing emphasis on the need to analyze tourist communication content, as well as being potentially useful for public policy with regard to heritage management.

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

New approaches to modeling primate socioecology: Does small female group size BEGET loyal males?

Humans are unusual in having stable male-female breeding bonds within multi-level societies. Such societies are not found in other apes, but have evolved independently in other primates, including several African papionins: hamadryas and Guinea baboons and gelada monkeys. Stable breeding bonds have been proposed to evolve either (1) because males can monopolize females when food distribution forces females to forage in small groups or (2) because females exchange exclusive mating for male services, such as protection from infanticide. Comparative studies are needed to test these hypotheses. In the meantime, we used an agent-based computer model to test the plausibility of these hypotheses. We simulated primates living in social groups within a larger population using a model we call BEGET (Behavior, Ecology, Genetics, Evolution, and Tradeoffs), which employed decision vectors, experimental evolution, realistic trade-offs, and phenotypic plasticity. We employed experimental evolution to generate male genotypes that varied in their competitive ability and in their long-term mating strategy. “Rover” males searched for and mated with any sexually receptive females whereas “Loyalist” males formed stable associations with particular groups of females. Much like living primates, the virtual primates exhibited tradeoffs between contest and scramble competition for access to females: Loyalists evolved larger body size than Rovers. We tested the effect of female foraging group size and the presence of infanticide and infant protection on the relative success of these strategies. We found that Loyalists achieved greater reproductive success than Rovers only when females were in groups smaller than four. Both Rovers and Loyalists sometimes evolved infanticidal behavior, but the presence of infanticide benefited Rovers rather than Loyalists, suggesting that the evolution of stable breeding bonds depends on the spatial distribution of females, rather than the risk of infanticide.

Spatially Heterogeneous Vaccine Coverage and Externalities in a Computational Model of Epidemics

We develop an agent-based computational model of epidemics with varying amounts of heterogeneity in regional vaccination coverage. We examine the severity of the resulting epidemics and the externalities created between high and low vaccination regions. The resulting size of the epidemics depends upon the heterogeneity of coverage, the interaction structure in the population, and how close the population is to herd immunity. Holding overall vaccination levels constant, the mean epidemic size increases and a larger level of vaccinations is needed to reach herd immunity when there is greater variability in the regional vaccination rates as long as the vaccination level is sufficiently high. This relationship amplifies when the contacts of individuals in the model have a greater share of inter-regional linkages. However, when inter-regional linkages and vaccination levels are sufficiently low, the mean epidemic size can decrease when there is greater variability in the regional vaccination rates. Further, we find that the magnitude of externalities between regions with different levels of vaccine coverage depends crucially on how close the population is to herd immunity. If population level vaccination coverage is sufficiently high (low) shifting connections from low coverage regions to high coverage regions will decrease (increase) mean epidemic size.