Intelligent simulation of coastal ecosystems

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto, Faculdade de Ciência e Tecnologia. Universidade Fernando Pessoa. 201

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

An Agent-Based Model of Multifunctional Agricultural Landscape Using Genetic Algorithms

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent.Multifunctional agriculture, Agent based modeling, Genetic Algorithm, Environmental Economics and Policy, Land Economics/Use,

Optimization of Agro-Socio-Hydrological Networks under Water Scarcity Conditions: Inter- and Trans-disciplinary Approaches for Sustainable Water Resources Management

Sustainable agriculture is one of the greatest challenges of our time. The pathways to sustainable agriculture consist of successive decisions for optimization that are often a matter of negotiation as resources are shared at all levels. This work essentially comprises three research projects with novel inter- and transdisciplinary methods to better understand and optimize agricultural water management under water scarcity conditions. In the first project, climate variability in the US Corn Belt was analyzed with a focus on deficit irrigation to find the optimal irrigation strategies for possible future changes. Two optimization methods for deficit irrigation showed positive water savings and yield increases in the predicted water scarcity scenarios. In the second project, a serious board game was developed and game sessions were carried out to simulate the complex decision space of actors in irrigated agriculture under climate and groundwater variability. The aim of the game was to understand how decisions are made by actors by observing the course of the game and linking these results to common behavioral theories implemented in socio-ecological models. In the third project, two frameworks based on innovation theories and agro-social-hydrological networks were developed and tested using agent-based models. In the first framework, centralized and decentralized irrigation management in Kansas US was compared to observe the development of collective action and the innovation diffusion of sustainable irrigation strategies. The second framework analyzed different decision processes to perform a sensitivity analysis of innovation implementation, groundwater abstraction and saline water intrusion in the Al Batinah region in Oman. Both frameworks allowed the evaluation of diverse behavior theories and decision-making parameters to find the optimal irrigation management and the impact of diverse socio-ecological policies. Inter- and Trans-disciplinary simulations of the interactions between human decisions and water systems, like the ones presented in here, improve the understanding of irrigation systems as anthropogenic landscapes in socio-economic and ecological contexts. The joint application of statistical and participatory approaches enables different but complementary perspectives that allow for a multidimensional analysis of irrigation strategies and water resources management.:Contents Declaration of Independent Work i Declaration of Conformity iii List of Publications v Acknowledgments ix Abstract xi Zusammenfassung xiii Contents xv List of Figures xvii List of Tables xix List of Abbreviations xxi 1. Introduction 3 1.1 Complex Networks Approach 3 1.2 Research Objectives 4 1.3 Thesis Outline 5 2. Literature Review 9 2.1 Agro-Hydrological Systems 9 2.1.1 Necessary Disciplinary Convergence 9 2.1.2 Multi-Objective Optimization Approaches 10 2.2 Optimization of Crop-Water Productivity 11 2.2.1 Irrigation Strategies 11 2.3 Sustainable Management of A-S-H Networks 12 2.3.1 Socio-Hydrology 13 2.3.2 Representation of Decision-Making Processes 14 2.3.3 Influence of Social Network 16 2.4 Socio-Hydrological Modeling Approaches 17 2.4.1 Game Theory Approach 17 2.4.2 Agent-Based Modeling 18 2.4.3 Participatory Modeling 20 2.5 Education for Sustainability 21 2.5.1 Experiential Learning 21 2.5.2 Serious Games 22 2.6 Summary of Research Gaps 24 3. Irrigation Optimization in The US Corn Belt 27 3.1 Agriculture in The Corn Belt 27 3.2 Historical and Prospective Climatic Variability 29 3.3 Simulated Irrigation Strategies 29 3.4 Optimal Irrigation Strategies Throughout the Corn Belt 30 3.5 Summary 31 4. Participatory Analysis of A-S-H Dynamics 35 4.1 Decision-Making Processes in A-S-H Networks 36 4.1.1 Collaborative and Participatory Data Collection Approaches 37 4.2 MAHIZ 38 4.2.1 Serious Game Development 38 4.2.2 Implementation of Serious Game Sessions 39 4.4 Evaluation of The Learning Process in Serious Games 40 4.5 Evaluation of Behavior Theories and Social Parameters 42 4.6 Summary 43 5 Robust Evaluation of Decision-Making Processes In A-S-H Networks 47 5.1 Innovation in A-S-H Networks 47 5.1.1 Multilevel Social Networks 48 5.1.2 Theoretical Framework of Developed ABMs 49 5.2 DInKA Model: Irrigation Expansion in Kansas, US 50 5.2.1 Robust Analysis of Innovation Diffusion 53 5.3 SAHIO Implementation: Coastal Agriculture in Oman 54 5.3.1 SAHIO Sensitivity analysis 58 5.4 Summary 60 6 Conclusions and Outlook 63 6.1 Limitations 64 6.2 Outlook 64 Bibliography 69 Appendix A. Implementation Code 79 A.1 DInKA 79 A.2 SAHIO 82 Appendix B. SAHIO’s Decision-Making Process for Each MoHuB Theory 91 Appendix C. SAHIO A-S-H Innovation Results 97 Appendix D. Selected Publications 101 D.1 Evaluation of Hydroclimatic Variability and Prospective Irrigation Strategies in the U.S. Corn Belt. 103 D.2 A Serious Board Game to Analyze Socio-Ecological Dynamics towards Collaboration in Agriculture. 121 D.2.1 MAHIZ Rulebook 140 D.2.2 MAHIZ Feedback Form 15

The Impact of Agent-Based Models in the Social Sciences after 15 Years of Incursions

This paper provides an overview on the impact of agent-based models in the social sciences. It focuses on the reasons why agent-based models are seen as important innovations in the recent decades. It is aimed to evaluate the impact of this innovation on various disciplines, such as economics, sociology, anthropology, and behavioural sciences. It discusses the advances it contributed to achieve and illustrates some comparatively new fields to which it gave rise. Finally, it emphasizes some research issues that need to be addressed in the future

The Future of Agent-Based Modeling

In this paper, I elaborate on the role of agent-based (AB) modeling for macroeconomic research. My main tenet is that the full potential of the AB approach has not been realized yet. This potential lies in the modular nature of the models, which is bought by abandoning the straitjacket of rational expectations and embracing an evolutionary perspective. I envisage the foundation of a Modular Macroeconomic Science, where new models with heterogeneous interacting agents, endowed with partial information and limited computational ability, can be created by recombining and extending existing models in a unified computational framework