Sensitivity analysis for agent-based models : a low complexity test-case

Methodologies for sensitivity analysis are considered to be of great importance for analyzing agent-based models (ABMs), even more because calibration and validation of ABMs often prove problematic. Different methodologies for sensitivity analysis may help to understand ABM dynamics and (thus) aid in the calibration and validation of ABMs. However, model complexity of ABMs is a significant hinderance for a detailed research on which (combination of) sensitivity analysis methods may provide the best option. We present here an agent-based model of low complexity to be used as test case for different methodologies of sensitivity analysis

Geosimulation and Multicriteria Modelling of Residential Land Development in the City of Tehran: A Comparative Analysis of Global and Local Models

Conventional models for simulating land-use patterns are insufficient in addressing complex dynamics of urban systems. A new generation of urban models, inspired by research on cellular automata and multi-agent systems, has been proposed to address the drawbacks of conventional modelling. This new generation of urban models is called geosimulation. Geosimulation attempts to model macro-scale patterns using micro-scale urban entities such as vehicles, homeowners, and households. The urban entities are represented by agents in the geosimulation modelling. Each type of agents has different preferences and priorities and shows different behaviours. In the land-use modelling context, the behaviour of agents is their ability to evaluate the suitability of parcels of land using a number of factors (criteria and constraints), and choose the best land(s) for a specific purpose. Multicriteria analysis provides a set of methods and procedures that can be used in the geosimulation modelling to describe the behaviours of agents. There are three main objectives of this research. First, a framework for integrating multicriteria models into geosimulation procedures is developed to simulate residential development in the City of Tehran. Specifically, the local form of multicriteria models is used as a method for modelling agents’ behaviours. Second, the framework is tested in the context of residential land development in Tehran between 1996 and 2006. The empirical research is focused on identifying the spatial patterns of land suitability for residential development taking into account the preferences of three groups of actors (agents): households, developers, and local authorities. Third, a comparative analysis of the results of the geosimulation-multicriteria models is performed. A number of global and local geosimulation-multicriteria models (scenarios) of residential development in Tehran are defined and then the results obtained by the scenarios are evaluated and examined. The output of each geosimulation-multicriteria model is compared to the results of other models and to the actual pattern of land-use in Tehran. The analysis is focused on comparing the results of the local and global geosimulation-multicriteria models. Accuracy measures and spatial metrics are used in the comparative analysis. The results suggest that, in general, the local geosimulation-multicriteria models perform better than the global methods

Modeling The Spatiotemporal Dynamics Of Cells In The Lung

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell\u27s behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate

Modeling dynamic community acceptance of mining using agent-based modeling

This research attempts to provide fundamental understanding into the relationship between perceived sustainability of mineral projects and community acceptance. The main objective is to apply agent-based modeling (ABM) and discrete choice modeling to understand changes in community acceptance over time due to changes in community demographics and perceptions. This objective focuses on: 1) formulating agent utility functions for ABM, based on discrete choice theory; 2) applying ABM to account for the effect of information diffusion on community acceptance; and 3) explaining the relationship between initial conditions, topology, and rate of interactions, on one hand, and community acceptance on the other hand. To achieve this objective, the research relies on discrete choice theory, agent-based modeling, innovation and diffusion theory, and stochastic processes. Discrete choice models of individual preferences of mining projects were used to formulate utility functions for this research. To account for the effect of information diffusion on community acceptance, an agent-based model was developed to describe changes in community acceptance over time, as a function of changing demographics and perceived sustainability impacts. The model was validated with discrete choice experimental data on acceptance of mining in Salt Lake City, Utah. The validated model was used in simulation experiments to explain the model\u27s sensitivity to initial conditions, topology, and rate of interactions. The research shows that the model, with the base case social network, is more sensitive to homophily and number of early adopters than average degree (number of friends). Also, the dynamics of information diffusion are sensitive to differences in clustering in the social networks. Though the research examined the effect of three networks that differ due to the type of homophily, it is their differences in clustering due to homophily that was correlated to information diffusion dynamics --Abstract, page iii

Socio-hydrology from Local to Large Scales: An Agent-based Modeling Approach

For decades, the interaction between water and people has attracted hydrologists’ attention. However, the coevolution of social and natural processes, which occurs across a range of time scales, has not yet been adequately characterized. This research gap has motivated more research in recent years under the umbrella of “socio-hydrology”. The purpose of socio-hydrology is to posit the endogeneity of humans in a hydrological system and then to investigate feedback mechanisms between hydrological and human systems that might lead to emergent phenomena. The current state-of-the-art in socio-hydrology faces several challenges that include (1) a tenuous connection of socio-hydrology to broader research on social, economic, and policy aspects of water resources, (2) the (in)capability of socio-hydrological models to capture human behavior by generic feedback mechanisms that can be extrapolated to other places, and (3) unsatisfying calibration or validation processes in modeling. To address the first gap, a socio-hydrology study needs to connect proper social theories on water-related human decision making with a water resource model based on a given context and scale. Addressing the second gap calls for socio-hydrology research with case studies in different and contrasting regions and at different scales. In fact, such study can shed light on the similarities and differences in socio-hydrological systems in different contexts and scales as initial steps for future research. The third research gap calls for a socio-hydrology study that improves calibration and validation processes. Thus, to address all these gaps in one thesis, two case studies with completely different environments are chosen to investigate various phenomena at different scales. The research presented here contributes to socio-hydrological understanding at two spatial scales. To account for the heterogeneity of human decision making and its interactions with the hydrologic system, an agent-based modeling (ABM) approach is used in this research. The first objective is to explore human adaptation to drought as well as the subsequent expected or unexpected effects on the agricultural sector and to develop a socio-hydrological model to predict agricultural water demand. To do so, an agent-based agricultural water demand model (ABAD) is developed. This model is applied to the Bow River Basin in Alberta, Canada, as a study region, which has recently experienced drought periods. The second objective is to explore conflict-and-cooperation processes in transboundary rivers as socio-hydrological phenomena at a large scale. The Eastern Nile Basin Socio-hydrological (ENSH) model is developed and applied to the Eastern Nile Basin (ENB) in Africa in which conflict-and-cooperation dynamics can be seen among Egypt, Sudan, and Ethiopia. The ENSH model aims to quantify and simulate these countries’ willingness to cooperate in the ENB. ABAD demonstrates (1) how farmers’ attitudes toward profits, risk aversion, environmental protection, social interaction, and irrigation expansion explain the dynamics of the water demand and (2) how the conservation program may paradoxically lead to the rebound phenomenon whereby the water demand may increase after decreasing through modernized irrigation systems. Through the ABAD model analysis, economic factors are found to dominantly control possible rebounds. Based on the insights gained via the model analysis, it is discussed that several strategies, including community participation and water restrictions, can be adopted to avoid the rebound phenomenon in irrigation systems. Fostering farmers’ awareness about the average water use in their community could be a means to avoid the rebound phenomenon through community participation. Also, another strategy to avoid the rebound phenomenon could be to reassign water allocations to reduce farmers’ water rights. The ENSH model showed that (1) socio-political factors (i.e., relative political stability and foreign direct investment) can explain two historical trends (i.e., (a) fluctuations in Ethiopia’s willingness to cooperate between 1983 and 2009 and (b) a decreasing Ethiopia’s willingness to cooperate between 2009 and 2016); (2) the 2008 food crisis (i.e., Sudan’s food gap) may account for Sudan recovering its willingness to cooperate; and (3) Egypt’s political (in)stability plays a role in its willingness to cooperate. The outcomes of this research can provide valuable insights to support policymakers for the long-term sustainability of water planning. This research investigates two main socio-hydrological phenomena at different spatial scales: the agricultural rebound phenomenon at a small geographical scale and the conflict and cooperation phenomena at a large geographical scale. The emergence of these phenomena can be a complex resultant of interaction and feedback mechanisms between the social system at the individual, institutional, and society levels and the hydrological system. Through developing quantitative socio-hydrological models, this research investigates the feedback mechanisms that may lead to the rebound phenomenon at a small scale and the conflict and cooperation phenomenon at a large scale. Finally, the research shows how these socio-hydrological models can be used for sustainable water management to avoid negative long-term consequences

The impacts of cash transfer programs on rural livelihoods: a study of Caboclos in the Brazilian Amazon estuary region

Rural households that rely on agricultural and natural resources for their livelihoods have been exposed to increasing socio-economic and climatic challenges over the past few decades, which requires urgent scientific exploration to effectively inform policies and other interventions. This dissertation investigates the rural livelihood of smallholders and the impacts of cash transfer programs through the use of empirical analysis and agent-based modelling and simulation (ABM) of the Caboclos in the Brazilian Amazon estuary region. The findings in this dissertation deepen the understanding of the livelihood dynamics of small farming households, provide insight about modelling uncertainty, and evaluate the impacts of policies and other approaches meant to alleviate poverty and enhance resilience. First, the empirical patterns of rural livelihoods, with a focus on the heterogeneous impacts from cash transfer programs, have been captured through statistical analysis of a household survey. Households were classified based on the amount of cash transfer and dependence on cash transfers to demonstrate the heterogeneity in this significant income of rural livelihoods. The results show the high level of heterogeneity among the value of cash transfers that households receive and in the households’ level of reliance on this stipend. Results also illustrate the differences among household characteristics and their significance regarding the degree of household reliance on cash transfers. Second, we constructed an ABM with an ensemble approach to represent the small farming households and simulate their livelihood outcomes with government cash transfer programs under eight experiments that differentiate main livelihood factors. The three ensemble members reflect a range of household behaviors, which include Max Profit (optimizing net economic returns), Max Leisure (pursuing optimal leisure time once subsistence is met), and Subsistence First (a strategy that maintains subsistence requirement first and then pursues market profit). Sensitivity and post-hoc analyses reveal the variability in the outcomes among three decision regimes, where the decision regime proves to be the most significant factor for livelihood outcomes at both the community level and individual level. The mere presence of cash transfers largely increases income and the equality of income distribution, of which the most drastic change occurs in the Max Leisure decision regime. However, household characteristics influence household livelihood outcomes differently within each decision regime. Third, we explored rural household livelihood and poverty dynamics using the ABM through the lens of development resilience. Various external shocks were applied to the household agents and their livelihood dynamics, particularly their resilience attribute, were analyzed. Our results first support the existence of the poverty trap and the relatively better-off zone as the “basin of attraction” that were proposed in resilience theory. Results from the simulation also indicate that external shocks, although similar in duration and magnitude, have significantly different impacts on livelihood resilience, with climate shocks being the most influential. Government cash transfer programs are more likely to be effective with a big initial capital boost, and a Subsistence First strategy, relative to Max Profit and Max Leisure strategies, is most likely to be resilient for vulnerable households, but not in households who are close to being trapped

Advancing PSS with complex urban systems sciences and scalable spatio-temporal models

Planning Support System (PSS) with a core of dynamic spatio-temporal model has been developed as analytical and information tools to aid and inform urban planning processes. However, scholarly communities identify that PSS has yet been popularized in planning practices, and not fully capable of meeting the challenge of understanding complex urban environments. I am dedicated to investigate and break through the bottlenecks of PSS with my experiences with University of Illinois Landuse Evolution and Impact Assessment Model (LEAM) PSS, which exemplify a PSS that that aid the process of collaboratively building spatio-temporal scenario models and transferring the knowledge to planning practitioners. I explore the future applications of PSS including Smart Cities, sentience, resilience, and environmental planning processes and their role in improving PSS usefulness in the practice of planning. PSS improvements will be presented in terms of multi-directional spatio-temporal processes and scenario planning. Moreover, I will address the process of transferring knowledge to users on model validity and ‘goodness-of-fit’ in real world planning applications. Beyond the traditional theoretical framework of PSS, the emerging Complex Urban System Sciences (CUS) challenge the core assumptions of spatial models of PSS, and pose opportunities for updating current PSS approaches into scalable spatio-temporal model that adheres to CUS principles. I will analyze this potential infusion by examining next generation PSSs within a framework of current CUS theories and advancement in statistical and computational methods. Case studies involved in my dissertation include LEAM PSS’ applications in McHenry County (IL), Peoria (IL), Chicago (IL), and St. Louis (MO). The final part of this dissertation highlights my contributions to the existing CUS theories. I will demonstrates how evidence from empirical applications can contribute to CUS theory itself. I will show how CUS can challenge the core assumptions of “distance to CBD” models that economists use to characterize urban structure and land-use

Complexity in land use change; the case of the Dutch dairy sector

Land use systems determine our well-being to a considerable extent, and therefore many policies have been formulated to regulate them. If land use systems are not fully understood, policies may fail or have unexpected consequences or even lead to sudden transitions in the land use system. This PhD research investigates the causes of potential sudden transitions in land use systems and more specifically in the Dutch dairy sector. In the thesis I show that certain system properties in combination with certain policies have the potential to cause sudden transitions in the Dutch dairy sector and more general in land use systems. An example could be the abrupt stop in maintaining landscape elements -such as hedgerows- in a specific region as a consequence of a change in agri-environmental policy.</p

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc