Combining Agent-Based Models with Stochastic Differential Equations for Gene Regulatory Networks

Over the past decade, the number of studies that rely on agent-based modeling to explore the mechanisms that shape people’s marriage decisions has increased considerably. One reason why this approach has spread is that compared to other methods, agent-based modeling makes it easier to deal with the micro-macro problem that family researchers face: namely, that people’s partnering decisions are guided by their personal preferences, but their ability to realize these preferences is constrained by the social context in which they are embedded; and, at the same time, each marriage and each divorce alters the context in which subsequent decisions take place. This creates complex feedback effects between the micro and macro levels that can be difficult to address with standard tools of analysis. Agent-based modeling makes it possible to study such feedback effects, first by implementing assumptions about people’s preferences and the contexts in which they make their marriage decisions in a formal model; and, subsequently, by studying the interplay of these effects in systematic simulation experiments. However, developing an agent-based model comes with its own challenges. For example, it can be difficult to decide precisely how people’s preferences and behavior should be formally represented. As overcoming these challenges can seem like a daunting task for novice modelers, there is a need to develop guidelines that can aid researchers in creating their own models. In this chapter, I aim to take a first step toward meeting this need. I review and compare the ways in which earlier studies have implemented existing marriage market theories in agent-based models. Based on my findings, I then formulate some guidelines that should make it easier for current and future generations of family scholars to apply agent-based modeling in their own work.

The current economic crisis has provoked an active response from the interdisciplinary scientific community. As a result many papers suggesting what can be improved in understanding of the complex socio-economics systems were published. Some of the most prominent papers on the topic include (Bouchaud, 2009; Farmer and Foley, 2009; Farmer et al, 2012; Helbing, 2010; Pietronero, 2008). These papers share the idea that agent-based modeling is essential for the better understanding of the complex socio-economic systems and consequently better policy making. Yet in order for an agent-based model to be useful it should also be analytically tractable, possess a macroscopic treatment (Cristelli et al, 2012). In this work we shed a new light on our research group's contributions towards understanding of the correspondence between the inter-individual interactions and collective behavior. We also provide some new insights into the implications of the global and local interactions, the leadership and the predator-prey interactions in the complex socio-economic systems.

Many types of financial time series, most notably market returns, have been found to exhibit long-range memory as well as dramatic day-to-day swings that cannot be adequately represented by light-tailed distributions such as the normal distribution. In particular, this means that for such time series, the usual variance parameter (i.e., the sum of covariances at all time lags) is not defined because the covariance function does not converge to zero fast enough as the time lag increases. Moreover in such time series, often the tails of the marginal density converge to zero so slowly that higher-order marginal moments such as skewness and kurtosis fail to exist. Therefore conventional methods for analyzing simulation-generated time series cannot generally be applied to high-fidelity simulations of financial markets.Building on earlier work in fractal geometry and fractal time series, Mandelbrot et al. proposed the multifractal model of asset returns (MMAR) as an alternative to the ARCH models for analyzing time series exhibiting volatility clustering, long-range dependence, and heavy-tailed returns (Mandelbrot et al. 1997). They defined the multifractal spectrum as the renormalized probability density function of the Holder exponents observed in the time series; and they used the multifractal spectrum to measure the ability of MMAR to match the statistical properties of real data. In 2002 Kantelhardt et al. formulated multifractal detrended fluctuation analysis (MF-DFA), an algorithm for extracting the multifractal spectrum from a time series (Kantelhardt et al. 2002).Many economists have recently adopted agent-based simulation for modeling financial markets. Fads based on new products, shocks related to world events, scandals involving company leaders, or outright criminal activity can drastically change the processes and relationships governing a market. Already there is evidence that agent-based models yield more accurate approximations to the true or observed behavior in financial markets. Agent-based models exhibit emergent behaviors that have been linked to non-Gaussian interaction metrics and singularities in the time series they generate (Chan 2011). Farmer et al. constructed a agent-based model that is capable of exhibiting many of the statistical properties observed in real financial data simply due to the rules governing a double auction order book (Farmer et al. 2005).During the 20th century, economists relied heavily on Brownian motion to construct theoretical models of finance. The advantages of using Brownian motion stem from its foundation in the Gaussian distribution; and with advances in stochastic calculus and differential equations, it became a staple in models of empirical finance. The well-known Black-Scholes model for options pricing is a prime example of this application. Multifractal analysis has the ability to illuminate the underlying difference between a monofractal like Brownian motion and a considerably more diverse construction like the multifractal binomial measure. Thus, combining multifractal analysis with an agent-based model has the potential to provide insight into the underlying processes and behaviors of a dynamic economic system that is constantly changing and evolving.To analyze market-return time series exhibiting volatility clustering, long-range dependence, or heavy-tailed marginals, we exploit multifractal analysis and agent-based simulation. We develop a robust, automated software tool for extracting the multifractal spectrum of a time series based on MF-DFA. Guidelines are given for setting MF-DFA's parameters in practice. The software is tested on simulated data with closed-form monofractal and multifractal spectra as well as on observed data, and the results are analyzed. We also present a prototype agent-based financial market model based on the zero-intelligence model of Farmer et al. and analyze its output using MF-DFA. The figure below compares the multifractal spectrum of the output from the prototype model with that of the multifractal binomial measure. The ultimate objective is to expand this model to study the effects of microlevel agent behaviors on the macrolevel time series output as analyzed by MF-DFA. Finally we explore the potential for validating agent-based models using MF-DFA and thus being able to tune these models to the multifractal spectrum of empirical data.

Agent-based models provide a formidable tool for exploring complex and emergent behaviour of biological systems as well as accurate results but with the drawback of needing a lot of computational power and time for subsequent analysis. On the other hand, equation-based models can more easily be used for complex analysis in a much shorter timescale. This paper formulates an ordinary differential equations and stochastic differential equations model to capture the behaviour of an existing agent-based model of tumour cell reprogramming and applies it to optimization of possible treatment as well as dosage sensitivity analysis. For certain values of the parameter space a close match between the equation-based and agent-based models is achieved. The need for division of labour between the two approaches is explored.

Purpose: This study provides an introduction to the agent-based modeling approach in industrial economics, with a focus on the Kuwaiti housing market structure. It develops and analyzes an illustrative agent-based market model.Study design/methodology/approach: We construct an agent-based model to study the effects of search costs and the number of speculators on housing price volatility. We also illustrate the agent-based modeling process using NetLogo and R.Sample and data: Our data consists of housing-related policies passed in Kuwait over the last two decades. We discuss relevant studies from industrial economics and housing literature based on their applicability to Kuwait’s housing market structure.Results: Our findings suggest that both lower search costs and a higher number of speculators lead to lower price volatility. We also demonstrated that agent-based modeling has significant potential for assessing policy impacts on housing markets. Featuresfrom other industrial economics models can be incorporated into the Kuwaiti housing market framework.Originality/value: This study bridges a gap in the literature by demonstrating how agent-based models can enhance understanding of policy effects on Kuwait’s housing market structure. It also highlights the benefits of integrating elements from industrialeconomics models into agent-based housing models.Research limitations/implications: While agent-based models offer potential applications in Kuwait, their design, testing, and validation require interdisciplinary efforts.

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.

Agent-based models have been developed and widely employed to assess the impact of disturbances or conservation management on animal habitat use, population development, and viability. However, the direct impacts of canopy disturbance on the arboreal movement of individual primates have been less studied. Such impacts could shed light on the cascading effects of disturbances on animal health and fitness. Orangutans are an arboreal primate that commonly encounters habitat quality deterioration due to land-use changes and related disturbances such as forest fires. Forest disturbance may, therefore, create a complex stress scenario threatening orangutan populations. Due to forest disturbances, orangutans may adapt to employ more terrestrial, as opposed to arboreal, movements potentially prolonging the search for fruiting and nesting trees. In turn, this may lead to changes in daily activity patterns (i.e., time spent traveling, feeding, and resting) and available energy budget, potentially decreasing the orangutan's fitness. We developed the agent-based simulation model BORNEO (arBOReal aNimal movEment mOdel), which explicitly describes both orangutans' arboreal and terrestrial movement in a forest habitat, depending on distances between trees and canopy structures. Orangutans in the model perform activities with a motivation to balance energy intake and expenditure through locomotion. We tested the model using forest inventory data obtained in Sebangau National Park, Central Kalimantan, Indonesia. This allowed us to construct virtual forests with real characteristics including tree connectivity, thus creating the potential to expand the environmental settings for simulation experiments. In order to parameterize the energy related processes of the orangutans described in the model, we applied a computationally intensive evolutionary algorithm and evaluated the simulation results against observed behavioral patterns of orangutans. Both the simulated variability and proportion of activity budgets including feeding, resting, and traveling time for female and male orangutans confirmed the suitability of the model for its purpose. We used the calibrated model to compare the activity patterns and energy budgets of orangutans in both natural and disturbed forests . The results confirm field observations that orangutans in the disturbed forest are more likely to experience deficit energy balance due to traveling to the detriment of feeding time. Such imbalance is more pronounced in males than in females. The finding of a threshold of forest disturbances that affects a significant change in activity and energy budgets suggests potential threats to the orangutan population. Our study introduces the first agent-based model describing the arboreal movement of primates that can serve as a tool to investigate the direct impact of forest changes and disturbances on the behavior of species such as orangutans. Moreover, it demonstrates the suitability of high-performance computing to optimize the calibration of complex agent-based models describing animal behavior at a fine spatio-temporal scale (1-m and 1-s granularity).

Flood risk management in Sint Maarten – A coupled agent-based and flood modelling method

PurposeUneven distribution and mistarget beneficiaries are among problems encountered during post-disaster relief operations in 2010 Mount Merapi eruption. The purpose of this paper is to develop an empirically founded agent-based simulation model addressing the evacuation dynamics and to explore coordination mechanism and other promising strategies during last-mile relief delivery.Design/methodology/approachAn agent-based model which was specified and parameterized by empirical research (interviews and survey) was developed to understand the mechanism of individual decision making underlying the evacuation dynamics. A set of model testing was conducted to evaluate confidence level of the model in representing the evacuation dynamics during post-disaster of 2010 Mount Merapi eruption. Three scenarios of last-mile relief delivery at both strategic and operational levels were examined to evaluate quantitatively the effectiveness of the coordination mechanism and to explore other promising strategies.FindingsResults indicate that the empirically founded agent-based modeling was able to reproduce the general pattern of observable Internal Displaced Persons based on government records, both at micro and macro levels, with a statistically non-significant difference. Low hazard perception and leader-following behavior which refuses to evacuate are the two factors responsible for late evacuation. Unsurprisingly, coordination through information sharing results in better performance than without coordination. To deal with both uneven distribution and long-term demand fulfillment, coordination among volunteers during aid distribution (at downstream operation) is not sufficient. The downstream coordination should also be accompanied with coordination between aid centers at the upstream operation. Furthermore, the coordination which is combined with other operational strategies, such as clustering strategy, using small-sized trucks and pre-positioning strategy, seems to be promising. It appears that the combined strategy of coordination and clustering strategy performs best among other combined strategies.Practical implicationsThe significant role of early evacuation and self-evacuation behavior toward efficient evacuation indicates that human factor (i.e. hazard perception and cultural factor) should be considered in designing evacuation plan. Early warning system through both technology and community empowerment is necessary to support early evacuation. The early warning system should also be accompanied with at least 69 percent of the population performing self-evacuation behavior for the effective evacuation. As information sharing through coordination is necessary to avoid redundant efforts, uneven distribution and eventually to reduce unmet demand, the government can act as a coordinating actor to authorize the operation and mobilize the resources. The combination of coordination and another strategy reducing lead time such as clustering analysis, thus increasing responsiveness, is seemly strategy for efficient and effective last-mile relief distribution.Originality/valueLiterature on coordination is dominated by qualitative approach, which is difficult to evaluate its effectiveness quantitatively. Providing realistic setting of the evacuation dynamics in the course of the 2010 Mount Merapi eruption, the empirically founded agent-based model can be used to understand the factors influencing the evacuation dynamics and subsequently to quantitatively examine coordination mechanisms and other potential strategies toward efficient and effective last-mile relief distribution.

This chapter introduces agent-based models and their application to management. The use of agent-based models is especially beneficial in situations where there are a number of inter-connected agents forming a complex system, be they firms, employees, customers, or other entities. We discuss the concept of agentbased modeling, building on an example model of interacting firms and customers. We discuss how this model was developed using the agent-based modeling toolkit RePast. We describe not only the advantages but also the disadvantages of agent-based models over more traditional techniques. We introduce several other agent-based models from the more general social science literature, discuss the level of complexity that should be introduced into an agent-based model, and discuss how such a technique may make an impact on the manager or organization whose strategy is to endeavor to manage the complex.

In Silico Translation of Cellular and Molecular Mechanisms to Clinical Phenomena in Atheroma Development with an Agent Based Model

Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines.

The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.

This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.

The development of an Agent-Based Model (ABM) has proven highly effective for analyzing how the behavior of different agents leads to aggregated phenomena. Despite the challenges in creating such a model—including conceptualization, agent definition, relationship establishment, behavior design, programming, testing, validation, and reporting—the process allows for valuable testing and rethinking of strategies for enhancing the resilience of cultural landscapes, as the results offer significant insights into phenomena like drought. While not predictive, the observed trends can inform general analysis and highlight key areas for action to achieve specific goals. The RescueMe project developed an ABM that simulates three types of administration and underscores the impact of decision-making on territorial resilience, significantly influenced by timely policies and actions. The primary goal of the model is to simulate how farmers, agricultural plots, and decision-makers interact with each other and their environment, particularly under varying drought conditions. The model tests the hypothesis that decision-makers can intervene to mitigate the effects of drought by creating mechanisms that enhance plot resilience and/or attract new farmers safeguarding the values of the cultural landscapes. In this way, the ABM aims to develop a reflection and awareness-raising tool to allow cultural landscapes to consider the consequences of different climate change adaptation measures and behaviors. The impact chains co-created with the project case studies have been used as a basis for the modeling. The impact chain of drought on agriculture (impact of specific climatic hazards on a given sector) was selected due to its importance for a significant number of cultural landscapes, and the organigraphs created during the early stages of the project were used to help define the agents. The scenarios generated with the ABM simulate the impact of the behavior of agents on landscape resilience and potentially inform the definition of a serious game.