Derivation of Continuum Models from An Agent-based Cancer Model: Optimization and Sensitivity Analysis.

Summary Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models—an agent‐based model and a system dynamics model—both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.

Nowadays, most coastal regions face a potential risk of tsunami. The evacuation is one of the most effective mitigation procedures. However, there are always the part of evacuees (e.g. the tourist) who lack information of the evacuation map, which motivates us to focus on the problem of optimizing of guidance sign placement for tsunami evacuation. Concretely, we must find out an optimal sign placement in order to have as many evacuees as possible to reach shelters before tsunami arrival. In fact, most studies focus on two approaches: Agent-Based modeling and Equation-Based modeling. Each approach has its own pros and cons. While the Agent-Based modeling introduces an accurate but very slow model, the Equation-Based one provides a very fast but inaccurate model. The idea of this study is that we learn the accurate Agent-Based model and adapt it into very fast Equation-Based model in order to solve the optimizing problem. In this paper, we present clearly two models representing the two approaches and pros and cons of each model. We then propose a reinforcement learning approach for adapting complex Agent-Based model into a very fast Linear Model (representing Equation-Based modeling approach). By experimentation, our proposed approach shows that we can replace a slow complex model by a very fast model with an acceptable level of accuracy in order to solve optimizing problem.

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.

This paper discusses some issues concerning the idea of taking equation-based simulation models (EBM) as formal, mathematical specifications of agent-based models (ABM) for Social Simulation. The paper tries to identify some of the benefits that such point of view could bring to the development of ABM, as well as some of the preliminary problems that an ABM developer has to face, in order to guarantee the credibility of his model. The paper first identifies the benefit of the EBM support for the formal verification of the correction of the ABM, given the correction of the EBM. Then, it identifies a preliminary set of crucial implementation details that are not covered by EBM specifications, and that the simulation developer has to define to make the ABM operational, under the constraint of keeping the ABM model compatible with the EBM. In particular, the two issues of the local vs. global scope of the agents' perceptions and of the degree of personalization of the agents are identified as crucial ones. Some preliminary concepts are proposed to help to solve those two problems.

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.

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.

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).

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.

Mathematical models in systems biology can use quantitative techniques to study the integrated behaviors of biological systems in macro level. Agent-based modeling provides a qualitative framework, focusing on how each individual molecule behaves in micro level. We are motivated to combine their features together to describe the gene regulatory networks, in particular the emergence of macro phenomena from micro interactions. An agent-based model will be well defined as general as possible, including of many agents of various molecular species interacting in it. The mathematical premises, which are grounded in the micro behaviors of agents, can derive the chemical master equations and the stochastic differential equations for modeling the integrated behaviors of systems in macro level. Combing agent-based models with stochastic differential equations affords a more complete perspective on gent regulatory networks. In addition, the sources and magnitudes of deterministic dynamics and random noises are explicitly characterized.

Background: While there is a great deal of discussion about complex adaptive systems in the field of evaluation, little has been written about the expression of complex adaptive systems in terms of agent-based models, the execution of those models as computer simulations, or the tight integration of agent-based modeling with traditional evaluation methods. These topics need to be explored if evaluation is to move beyond using complex adaptive systems in an exclusively heuristic fashion. Purpose: This paper advocates for the integration of agent based modeling into traditional evaluation activities. We advance this position in order to spark a movement toward incorporating the formal application of complex adaptive systems into the theory and methods of evaluation. To make our case, we provide a hypothetical example of how interactions between an agent-based model and evaluation can provide unique and powerful understanding about the adoption of evidence-based practices in the field of addiction treatment. We advance our argument by addressing four questions: Why combine evaluation methods with simulation methods? Why use a complex adaptive systems approach? Why add agent-based modeling to the mix of evaluation tools? What is the relationship between agent-based modeling and the theories and methods of evaluation that are being developed to deal with surprise, unintended consequences, and program evolution? Setting: Not applicable. Intervention: Not applicable. Research Design: Not applicable. Data Collection and Analysis: Not applicable. Findings: Not applicable.

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.

Heat baths and computational agent-based models

Heat Baths and Computational Agent-Based Modeling

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