Agent-Based Modeling of Human-Induced Spread of Invasive Species in Agricultural Landscapes: Insights from the Potato Moth in Ecuador

Agent-based models (ABM) are ideal tools to deal with the complexity of pest invasion throughout agricultural socio-ecological systems, yet very few studies have applied them in such context. In this work we developed an ABM that simulates interactions between farmers and an invasive insect pest in an agricultural landscape of the tropical Andes. Our specific aims were to use the model 1) to assess the importance of farmers\' mobility and pest control knowledge on pest expansion and 2) to use it as an educational tool to train farmer communities facing pest risks. Our model combined an ecological sub-model, simulating pest population dynamics driven by a cellular automaton including environmental factors of the landscape, with a social model in which we incorporated agents (farmers) potentially transporting and spreading the pest through displacements among villages. Results of model simulation revealed that both agents\' movements and knowledge had a significant, non-linear, impact on invasion spread, confirming previous works on disease expansion by epidemiologists. However, heterogeneity in knowledge among agents had a low effect on invasion dynamics except at high levels of knowledge. Evaluations of the training sessions using ABM suggest that farmers would be able to better manage their crop after our implementation. Moreover, by providing farmers with evidence that pests propagated through their community not as the result of isolated decisions but rather as the result of repeated interactions between multiple individuals over time, our ABM allowed introducing them with social and psychological issues which are usually neglected in integrated pest management programs.Socio-Ecological Systems, Farmers, Invasive Pest, Long Distance Dispersion, Teaching

Coupled Information Diffusion–Pest Dynamics Models Predict Delayed Benefits of Farmer Cooperation in Pest Management Programs

Worldwide, the theory and practice of agricultural extension system have been dominated for almost half a century by Rogers' “diffusion of innovation theory”. In particular, the success of integrated pest management (IPM) extension programs depends on the effectiveness of IPM information diffusion from trained farmers to other farmers, an important assumption which underpins funding from development organizations. Here we developed an innovative approach through an agent-based model (ABM) combining social (diffusion theory) and biological (pest population dynamics) models to study the role of cooperation among small-scale farmers to share IPM information for controlling an invasive pest. The model was implemented with field data, including learning processes and control efficiency, from large scale surveys in the Ecuadorian Andes. Our results predict that although cooperation had short-term costs for individual farmers, it paid in the long run as it decreased pest infestation at the community scale. However, the slow learning process placed restrictions on the knowledge that could be generated within farmer communities over time, giving rise to natural lags in IPM diffusion and applications. We further showed that if individuals learn from others about the benefits of early prevention of new pests, then educational effort may have a sustainable long-run impact. Consistent with models of information diffusion theory, our results demonstrate how an integrated approach combining ecological and social systems would help better predict the success of IPM programs. This approach has potential beyond pest management as it could be applied to any resource management program seeking to spread innovations across populations

Simulating Inflation

Inflation is a common problem in modern economics, and it seems to persist, requiring government financial policy intervention on a regular basis in order to properly manage. The mechanics of inflation are difficult to understand, since the best metric modern society has for inflation involves taking samples of prices across the country. A simulation can give deeper insight into more than the mere fact that prices rise over time: a simulation can help to answer the “why” question. The problem with this statement is that developing a simulation is not as simple as writing some code. A simulation is developed according to a paradigm, and paradigms are not created equal. Research reveals that traditional paradigms that impose order from the outside do not mimic reality and thus do not give a good degree of accuracy. By contrast, Agent Based Modelling models reality very closely and is consequently able to simulate economic systems much more accurately

A unified framework for traditional and agent-based social network modeling

In the last sixty years of research, several models have been proposed to explain (i) the formation and (ii) the evolution of networks. However, because of the specialization required for the problems, most of the agent-based models are not general. On the other hand, many of the traditional network models focus on elementary interactions that are often part of several different processes. This phenomenon is especially evident in the field of models for social networks. Therefore, this chapter presents a unified conceptual framework to express both novel agent-based and traditional social network models. This conceptual framework is essentially a meta-model that acts as a template for other models. To support this meta-model, the chapter proposes a different kind of agent-based modeling tool that we specifically created for developing social network models. The tool the authors propose does not aim at being a general-purpose agent-based modeling tool, thus remaining a relatively simple software system, while it is extensible where it really matters. Eventually, the authors apply this toolkit to a novel problem coming from the domain of P2P social networking platforms

A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)

In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose

A Transportation Model For Demand Responsive Fleet Operation In A Manufacturing Firm

We study the problem of determining the number of vehicles needed to provide a demand responsive transit service with a predetermined quality for the user in terms of waiting time at the stops and maximum allowed detour. We propose a probabilistic model that requires only the knowledge of the distribution of the demand over the service area and the quality of the service in terms of time windows associated with pickup and delivery nodes. This methodology can be much more effective and straight forward compared to a simulation approach whenever detailed data on demand patterns are not available. Computational results under a fairly broad range of test problems show that the model can provide an estimation of the required size of the fleet in several different scenarios

An agent-based approach for modeling dynamics of contagious disease spread

Background: The propagation of communicable diseases through a population is an inherentspatial and temporal process of great importance for modern society. For this reason a spatiallyexplicit epidemiologic model of infectious disease is proposed for a greater understanding of thedisease\u27s spatial diffusion through a network of human contacts.Objective: The objective of this study is to develop an agent-based modelling approach theintegrates geographic information systems (GIS) to simulate the spread of a communicable diseasein an urban environment, as a result of individuals\u27 interactions in a geospatial context.Methods: The methodology for simulating spatiotemporal dynamics of communicable diseasepropagation is presented and the model is implemented using measles outbreak in an urbanenvironment as a case study. Individuals in a closed population are explicitly represented by agentsassociated to places where they interact with other agents. They are endowed with mobility,through a transportation network allowing them to move between places within the urbanenvironment, in order to represent the spatial heterogeneity and the complexity involved ininfectious diseases diffusion. The model is implemented on georeferenced land use dataset fromMetro Vancouver and makes use of census data sets from Statistics Canada for the municipality ofBurnaby, BC, Canada study site.Results: The results provide insights into the application of the model to calculate ratios ofsusceptible/infected in specific time frames and urban environments, due to its ability to depict thedisease progression based on individuals\u27 interactions. It is demonstrated that the dynamic spatialinteractions within the population lead to high numbers of exposed individuals who performstationary activities in areas after they have finished commuting. As a result, the sick individuals areconcentrated in geographical locations like schools and universities.Conclusion: The GIS-agent based model designed for this study can be easily customized to studythe disease spread dynamics of any other communicable disease by simply adjusting the modeleddisease timeline and/or the infection model and modifying the transmission process. This type ofsimulations can help to improve comprehension of disease spread dynamics and to take bettersteps towards the prevention and control of an epidemic outbreak

Network meta-metrics: Using evolutionary computation to identify effective indicators of epidemiological vulnerability in a livestock production system model

We developed an agent-based susceptible / infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node’s positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call “meta-metrics” — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena

Simulating epidemiological processes using community-structured scale-free networks

The transmission mechanisms of infectious diseases are well known and treated in the literature, but considering that the interactions during the transmission are complex, they may be simulated by computational models. The understanding of the disease dynamics is of great interest, since it makes possible to avoid implications and consequences of a possible outbreak. In this sense, the objective of this work is to present a model that employs scale-free networks with community structures to represent the interactions between individuals of a population. According to the results, the tool simulated successfully the spread of Influenza in a population of 6,500 individuals and proved consistent with parameters found in the literature. As main conclusion, we argue that the community structure in a contact network can significantly affects the disease dynamics

On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool