Agent-based Computational Model Research Articles

The "flat peer learning" agent-based model.

This paper deals with peer learning and, in particular, with the phenomena of exclusion; it proposes to model a group of learners where everyone has his own behaviour that expresses his way of following a curriculum. The focus is on individual motivations that avoid disadvantage certain individuals while optimising behaviour at the community level; in this context, the approach is based on the belief that the induced learning dynamics can be clarified by the contribution of agent-based modelling and its entry into the field of peer learning simulation. Flat learning means here that every learner features the same initial skill level, along with the same opportunities to learn both independently and with the help of peers. To address this topic the paper proposes the Flat Peer Learning agent-based computational model inspired by the Vygotsky’s social and learning theory. The paper shows that even if strict equity could be guaranteed, educators would still be faced with the dilemma of having to choose between optimising the learning process for the group or preventing exclusion for some.

Correction to: A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model

Correction to: A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model

A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model

The complex nature of agent-based modeling may reveal more descriptive accuracy than analytical tractability. That leads to an additional layer of methodological issues regarding empirical validation, which is an ongoing challenge. This paper offers a replicable method to empirically validate agent-based models, a specific indicator of “goodness-of-validation” and its statistical distribution, leading to a statistical test in some way comparable to the p value. The method involves an unsupervised machine learning algorithm hinging on cluster analysis. It clusters the ex-post behavior of real and artificial individuals to create meso-level behavioral patterns. By comparing the balanced composition of real and artificial agents among clusters, it produces a validation score in [0, 1] which can be judged thanks to its statistical distribution. In synthesis, it is argued that an agent-based model can be initialized at the micro-level, calibrated at the macro-level, and validated at the meso-level with the same data set. As a case study, we build and use a mobility mode-choice model by configuring an agent-based simulation platform called BedDeM. We cluster the choice behavior of real and artificial individuals with the same ex-ante given characteristics. We analyze these clusters’ similarity to understand whether the model-generated data contain observationally equivalent behavioral patterns as the real data. The model is validated with a specific score of 0.27, which is better than about 95% of all possible scores that the indicator can produce. By drawing lessons from this example, we provide advice for researchers to validate their models if they have access to micro-data.

Division of Labor Through Self-Selection

Self-selection–based division of labor has gained visibility through its role in varied organizational contexts such as nonhierarchical firms, agile teams, and project-based organizations. Yet, we know relatively little about the precise conditions under which it can outperform the traditional allocation of work to workers by managers. We develop a computational agent-based model that conceives of division of labor as a matching process between workers’ skills and tasks. This allows us to examine in detail when and why different approaches to division of labor may enjoy a relative advantage. We find a specific confluence of conditions under which self-selection has an advantage over traditional staffing practices arising from matching: when employees are very skilled but at only a narrow range of tasks, the task structure is decomposable, and employee availability is unforeseeable. Absent these conditions, self-selection must rely on the benefits of enhanced motivation or better matching based on worker’s private information about skills, to dominate more traditional allocation processes. These boundary conditions are noteworthy both for those who study as well as for those who wish to implement forms of organizing based on self-selection.

MARK-UP VS. INTEREST-BASED FINANCING ON GDP: AN APPLICATION OF AGENT-BASED COMPUTATIONAL MODEL

This study aims to introduce the application of artificial intelligence in the area of Islamic finance by examining the dynamic changes of gross domestic product (GDP) under fully markup-based financing. The application of artificial intelligence using agent-based computational model (ABM) is employed to conduct the simulation. The simulation result shows that the movement of GDP under markup-based financing which represents Islamic financial system has better performance compared to interest-based lending. In this regard, profit shared to depositors has positive impact on GDP which also proofs that Islamic banking system may promote sustainable economic growth and may create wealth for the whole society. This study proofs that Islamic bank essentially more stable than conventional bank and hence may fights against crisis. This study is potentially the initial work to examine the dynamic changes of economic growth under fully Islamic financial system by applying artificial intelligence concept as its methodology. Thus, this study is expected to contribute to the development of Islamic economics and finance research.

Macro-Regional Economic Structural Change Driven by Micro-founded Technological Innovation Diffusion: An Agent-Based Computational Economic Modeling Approach

This paper introduces an agent-based computational economic modeling approach into an input–output analytical framework and proposes diffusion of technological innovation behavior into the simulation models. A large number of heterogeneous firms with macro-regional economic frameworks are included to perform policy simulation scenarios to investigate the impact of diffusing technological innovations on the dynamic changes in the regional economic structures of major global economies (i.e., China, Japan, the United States, Russia, India, and the European Union). This study reveals that process innovation may be more conducive to promoting the transfer of resource elements between regions for China, the EU, Japan, India, and Russia. However, for the U.S., product innovation may facilitate upgrading its industrial structure. Furthermore, from 2012 to 2030, for these six economies, the output share of the primary industry will likely decline by varying degrees, while the output share of the tertiary industry will show an uptrend. The employment share in the tertiary industry in these six economies decreased. Another important finding is that differentiated technological innovation-driven policies must be adopted within the context of global economic governance. Moreover, each economy should choose a technological innovation mode that is suitable for its economic development. Thus, these findings provide an important theoretical basis for formulating global economic governance policies in the future.

Subnational Sustainable Development: The Role of Vertical Intergovernmental Transfers in Reaching Multidimensional Goals

Achieving sustainable development hinges on two critical factors: the subnational implementation of public policies and the efficient allocation of resources across regions through vertical intergovernmental transfers. We introduce a framework that links these two mechanisms for analyzing the impact of reallocating federal transfers in the presence of regional heterogeneity from development indicators, budget sizes, expenditure returns, and long-term structural factors. Our study focuses on the case of Mexico and its 32 states. Using an agent-based computational model, we estimate the development gaps that will remain by the year 2030, and characterize their sensitivity to changes in the states' budget sizes. Then, we estimate the optimal distribution of federal transfers to minimize these gaps. Crucially, these distributions depend on the specific development objectives set by the national government, and by various interdependencies between the heterogeneous qualities of the states. This work sheds new light on the complex problem of budgeting for the Sustainable Development Goals at the subnational level, and it is especially relevant for the study of fiscal decentralization from the expenditure point of view.

A three dimensional computer model of urothelium and bladder cancer initiation, progress and collective invasion

Bladder cancer is a common type of cancer, and one with high recurrence rate and overall monitoring and treatment costs. It was developed and tested a three-dimensional agent-based computational model of the urothelium, a purpose-built Cellular Potts Model, to describe both a healthy urothelium and the initiation, development and invasion stages of a bladder cancer. This work is focused on the identification of the conditions in which tumor cells can cross, by mechanical means, the basement membrane and invade the bladder lamina propria. It is demonstrated how specific mechanical properties of the cancer cells, as their stiffness and adhesion to neighboring cells, heavily determine the probability of transition from a non-invasive to an invasive tumor. In particular, it is shown how an increased cellular adhesion of tumor cells to neighbor cells and/or extracellular fibers drives a collective migration, increasing the tumor growth rate and its invasion capacity. It is proposed that these physical characteristics should be considered as potential therapeutic targets to control the tumor growth and its progress to a more dangerous condition.

Agent-Based Computational Modelling of Glioblastoma Predicts that Stromal Density is Central to Oncolytic Virus Efficacy

Agent-Based Computational Modelling of Glioblastoma Predicts that Stromal Density is Central to Oncolytic Virus Efficacy

Tracing resilience, social dynamics and behavioral change: a review of agent-based flood risk models

Climate change and rapid urbanization exacerbate flood risks worldwide. The recognition of the crucial role that human actors play in altering risks and resilience of flood-prone cities triggers a paradigm shift in climate risks assessments and drives the proliferation of computational models that include societal dynamics. Yet, replacing a representative rational actor dominant in climate policy models with a variety of behaviorally-rich agents that interact, learn, and adapt is not straightforward. Focusing on the costliest climate-exacerbated hazard, flooding, we review computational agent-based models that include behavioral change and societal dynamics. We distinguish between two streams of literature: one stemming from economics & behavioral sciences and another from hydrology. Our findings show that most studies focus on households while representing decisions of other agents (government, insurance, urban developers) simplistically and entirely overlooking firms' choices in the face of risks. The two communities vary in the extent they ground agents' rules in social theories and behavioral data when modeling boundedly-rational decisions. While both aspire to trace feedbacks that agents collectively instigate, they employ different learning and interactions when computing societal dynamics in the face of climate risks. Dynamics of hazard, exposure, and vulnerability components of flood risks driven by incremental adaptation of agents are well represented. We highlight that applying a complex adaptive system perspective to trace the evolution of resilience can lead to a better understanding of transformational adaptation. The methodological advances in computational models with heterogeneous behaviorally-rich adaptive agents are relevant for adaptation to different climate-driven hazards beyond flooding.

Tumor cell invasiveness in the initial stages of bladder cancer development - A computational study.

Bladder cancer is one of the most common types of cancer, being the sixth more frequent in men, and one with higher recurrence rates and overall treatment costs. We introduce an agent-based computational model of the urothelium, adopting a Cellular Potts Model (CPM) approach to describe both a healthy urothelium and the development of bladder cancer. We focus on the identification of the conditions in which cancer cells cross, by mechanical means, the basement membrane and invade the bladder lamina propria. When within the urothelium the tumor grows in a very constrained environment. These tight conditions imply that the urothelium layer where the tumor initiates greatly determines tumor growth and invasiveness. Moreover, we demonstrate how specific mechanical properties of the cancer cells, as their stiffness or the adhesion to neighboring cells, heavily modulate the critical initial moments of tumor development. We propose that these characteristics should be considered as therapeutic targets to control tumor growth.

FROM SELFISHNESS & ALTRUISM TO MACRO-BEHAVIOR

The central subject of this paper is the relation between the macro-level of the Society and the micro-level of its individuals. In complex real-life systems, this relationship is not trivial and even the result at the macro-level can be counter-intuitive. One of the first models that highlights this is the Sakoda and Schelling’s model of spatial segregation where a person uses a selfish rule to decide whether or not to leave his current place; on the basis of the ratio of “compatible” agents in the neighborhood of each individual, simulations have shown that tolerant agents group together beyond what their own tolerance imposes. This paper compares the macro-behavior arising from either selfish or altruist micro-motives; for this purpose, we propose agent-based computational models based, respectively, on selfishness, altruism and a mixture of both. Computational simulations show that (i) in a totally altruistic population, a vast majority becomes satisfied as if the agents were themselves selfish and end up spatially segregated; (ii) altruist micro-motives allow to reduce both the micro-macro gap and the segregation phenomenon; (iii) in a mixed population, the macro-effect depends both on the respective weight of each micro-motives and the allocation strategy.

Zakah in a Complex World

The Covid-19 pandemic hits the world economy. On July 2020, some countries have plunged into recession. The prescription from the perspective of Islamic economics and finance need to be formulated. The application conventional methodology like survey or any types of regression will not able to examine the dynamic changes on the economy during the occurrence of pandemic and will fail to draw the very long term situation. In this regard, Islamic economics needs to move forward to utilize the advancement of technology.
 The use of Agent-based Computational Model (ABM) is one of alternative methodology to develop research in the area of Islamic economics and finance. ABM is a computational study which builds the economy as a complex world, thus, the prescription for economic crisis based on Islamic views can be formulated.
 This research has two objectives, i.e., (a) to review on how ABM has developed research in the area of Islamic economics and finance, (b) to provide an example on how the use of ABM to develop a model on how zakah may minimize level of Gini index under interest based system.
 This research finds two main points. First, the number of ABM research in Islamic economics are limited, some of which are still at the development stage of the conceptual framework. Second, the ABM simulation results shows how zakah may minimize level of Gini index. Therefore, this research may be used as one of the references on how to develop and formulate a model in the area of Islamic economic and finance.

Heterogeneous speculators and stock market dynamics: a simple agent-based computational model

We propose a simple agent-based computational model in which speculators’ trading behavior may cause bubbles and crashes, excess volatility, serially uncorrelated returns, fat-tailed return distributions and volatility clustering, thereby replicating five important stylized facts of stock markets. Since each speculator bets on his own (technical and fundamental) trading signals, stock prices are excessively volatile and oscillate erratically around their fundamental value. However, speculators’ heterogeneity occasionally vanishes, e.g. due to panic-induced herding behavior, yielding extreme returns. Lasting regimes with high volatility originate from the fact that speculators extract stronger trading signals out of past stock price movements when stock prices fluctuate strongly. Simulations furthermore suggest that circuit breakers may be an effective tool to combat financial market turbulences.

Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability

A local disruption can propagate to forward and downward through the material flow and eventually influence the entire supply chain network (SCN). This phenomenon of ripple effect, immensely existing in practice, has received great interest in recent years. Moreover, forward and backward disruption propagations became major stressors for SCNs during the COVID-19 pandemic triggered by simultaneous and sequential supply and demand disruptions. However, current literature has paid less attention to the different impacts of the directions of disruption propagation. This study examines the disruption propagation through simulating simple interaction rules of firms inside the SCN. Specifically, an agent-based computational model is developed to delineate the supply chain disruption propagation behavior. Then, we conduct multi-level quantitative analysis to explore the effects of forward and backward disruption propagation, moderated by network structure, network-level health and node-level vulnerability. Our results demonstrate that it is practically important to differentiate between forward and backward disruption propagation, as they are distinctive in the associated mitigation strategies and in the effects on network and individual firm performance. Forward disruption propagation generally can be mitigated by substitute and backup supply and has greater impact on firms serving the assembly role and on the supply/assembly networks, whereas backward disruption propagation is normally mitigated by flexible operation and distribution and has bigger impact on firms serving the distribution role and on distribution networks. We further analyze the investment strategies in a dual-focal supply network under disruption propagation. We provide propositions to facilitate decision-making and summarize important managerial implications.

Rugged landscapes: complexity and implementation science

BackgroundMis-implementation—defined as failure to successfully implement and continue evidence-based programs—is widespread in public health practice. Yet the causes of this phenomenon are poorly understood.MethodsWe develop an agent-based computational model to explore how complexity hinders effective implementation. The model is adapted from the evolutionary biology literature and incorporates three distinct complexities faced in public health practice: dimensionality, ruggedness, and context-specificity. Agents in the model attempt to solve problems using one of three approaches—Plan-Do-Study-Act (PDSA), evidence-based interventions (EBIs), and evidence-based decision-making (EBDM).ResultsThe model demonstrates that the most effective approach to implementation and quality improvement depends on the underlying nature of the problem. Rugged problems are best approached with a combination of PDSA and EBI. Context-specific problems are best approached with EBDM.ConclusionsThe model’s results emphasize the importance of adapting one’s approach to the characteristics of the problem at hand. Evidence-based decision-making (EBDM), which combines evidence from multiple independent sources with on-the-ground local knowledge, is a particularly potent strategy for implementation and quality improvement.

EXPLORATION OF AGENT-BASED SIMULATION: THE MULTIPLIER EFFECT OF ZAKAH ON ECONOMIC GROWTH

Zakat plays an undeniably significant role in social life. The enforcement of zakah in early Islamic history is evidence that it is a powerful tool for fostering economic growth. The economy under the reign of Caliphate Umar Ibn Abdul Azis even achieved a surplus, with no one having the right to receive zakah. Recent studies have attempted to link the role of zakah and economic growth qualitatively by arguing that zakah can contribute to economic development. However, the extent to which it has a multiplier effect on economic growth has not been examined. Considering this research gap, this study examines such an effect on economic growth, as represented by Gross Domestic Product (GDP). To capture the full impact of zakah on aggregate production, two scenarios are conducted, namely the economy with and without zakah. The simulation results show that zakah can promote aggregate production. On the other hand, the economy experiences lower aggregate production when there is no zakah. An agent-based computational model (ABM) simulation is employed to run the simulation. The application of ABM in this study is intended to introduce the use of computational study as an alternative method of developing research in Islamic economics.

Decision-facilitating information in hidden-action setups: an agent-based approach

The hidden-action model captures a fundamental problem of principal-agent theory and provides an optimal sharing rule when only the outcome but not the effort can be observed (Holmström in Bell J Econ 10(1):74, 1979). However, the hidden-action model builds on various explicit and also implicit assumptions about the information of the contracting parties. This paper relaxes key assumptions regarding the availability of information included in the hidden-action model in order to study whether and, if so, how fast the optimal sharing rule is achieved and how this is affected by the various types of information employed in the principal-agent relation. Our analysis particularly focuses on information about the environment and about feasible actions for the agent. We follow an approach to transfer closed-form mathematical models into agent-based computational models and show that the extent of information about feasible options to carry out a task only has an impact on performance if decision makers are well informed about the environment, and that the decision whether to perform exploration or exploitation when searching for new feasible options only affects performance in specific situations. Having good information about the environment, on the contrary, appears to be crucial in almost all situations.

ROBUST MATHEMATICAL FORMULATION AND PROBABILISTIC DESCRIPTION OF AGENT-BASED COMPUTATIONAL ECONOMIC MARKET MODELS

In science and especially in economics, agent-based modeling has become a widely used modeling approach. These models are often formulated as a large system of difference equations. In this study, we discuss two aspects, numerical modeling and the probabilistic description for two agent-based computational economic market models: the Levy–Levy–Solomon model and the Franke–Westerhoff model. We derive time-continuous formulations of both models, and in particular, we discuss the impact of the time-scaling on the model behavior for the Levy–Levy–Solomon model. For the Franke–Westerhoff model, we proof that a constraint required in the original model is not necessary for stability of the time-continuous model. It is shown that a semi-implicit discretization of the time-continuous system preserves this unconditional stability. In addition, this semi-implicit discretization can be computed at cost comparable to the original model. Furthermore, we discuss possible probabilistic descriptions of time-continuous agent-based computational economic market models. Especially, we present the potential advantages of kinetic theory in order to derive mesoscopic descriptions of agent-based models. Exemplified, we show two probabilistic descriptions of the Levy–Levy–Solomon and Franke–Westerhoff model.

Coherent or conflicting? Assessing natural gas subsidy and energy efficiency policy interactions amid CO2 emissions reduction in Malaysia electricity sector

One of the negative environmental consequences of natural gas subsidy (NGS) is that it stimulates the substitution from low carbon generating technologies to natural gas, and thus hinders the timely electricity decarbonisation. To manage Malaysia’s energy consumption and consequently reduce its energy-related carbon dioxide (CO2) emission, there has been renewed interest in removing NGS. In policy debates, it is a commonly held belief that NGS stimulates excessive energy consumption, and thus phasing it out would reduce electricity-related greenhouse gas emissions. However, whether this is the real situation and the emission reduction potential of phasing out NGS along with energy efficiency measures are still unanswered. Here we build an agent-based computational economic model with six types of agents (electricity producer, government, consumer, environment, electricity, and fuel markets). A simulation toolbox is developed to explore the dynamics of the electricity sector under different scenarios between 2015 and 2050. It is shown that removing NGS alone cannot lead to low carbon emissions because its removal would only instigate the substitution from low carbon technologies to the coal-fired plants. Two average annual growth rates of electricity demand have been simulated for energy efficiency policies. The simulation results show that the lower the rate of annual demand growth, the lower the growth rate of electricity output, and ultimately, the lower the energy-related CO2 emissions. With these two policies alone, CO2 emissions will continue to grow. Our results finally demonstrate that for the Malaysian government to achieve its emission reduction targets, additional efforts and policies will be required.