From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity

An integrated socio-economic agent-based modeling framework towards assessing farmers' decision making under water scarcity and varying utility functions

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers' behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.

Investigating flood resilience perceptions and supporting collective decision-making through fuzzy cognitive mapping

Improving flood resilience of communities requires a holistic understanding of risks and resilience options as well as the preferences and priorities of different stakeholders. Innovations in risk and resilience assessment have helped communities to identify gaps in their flood risk management strategy but selecting and implementing resilience solutions remains a big challenge for many decision-makers. In addition to traditional appraisals and cost-benefit assessments this also calls for a participatory process in which various stakeholders are encouraged to adopt a system-level approach in identifying interventions that can maximise a range of benefits and co-benefits. In this study, we investigate how a combination of modelling and measurement methods can help decision-makers with their flood resilience strategies. We apply a participatory system thinking approach combining Fuzzy Cognitive Mapping (FCM) with a flood resilience measurement framework called Flood Resilience Measurement for Communities (FRMC). We first investigate stakeholders' biases on flood resilience interventions, and then lead them through a system thinking exercise using FCM and FRMC to elicit mental models representing important aspects of flood resilience and their interrelation. These are then aggregated, representing the collective perceptions and knowledge of stakeholders, and used to identify the most beneficial resilience actions in terms of direct and indirect impacts on flood resilience. We apply this approach to the case of Lowestoft, a coastal town in England exposed to significant flood risk. Developed in close collaboration with the local authorities, the ambition is to support decision-making on flood resilience interventions. We find that this combination of methods enables system-level thinking and inclusive decision-making about flood resilience which can ultimately encourage transformative decisions on prioritization of actions and investments.

Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model

The efficacy of land-use changes on aquatic ecosystems has been extensively studied in recent decades. Water resource management needs to understand the relationship between land-use change patterns and water quality, especially in urban areas. Hence, recognizing spatial-temporal changes in land use is required for sustainable development and proper water resource management. This research has developed an integrated model based on agent-based model (ABM) and multi-layer perceptron (MLP) neural network technique to predict the future land-use transformation tested on the North Ahvaz watershed, Iran. Random forest-supervised classification technique was applied to derive the land-use maps using Landsat 1989, 2004, and 2019 images in the Google Earth Engine (GEE) platform. The overall accuracy of classified land-use images was 0.82, 0.81, and 0.84, respectively, with the kappa coefficient of 0.74, 0.72, and 0.78. Land-use change analysis and generating transition potential maps were carried out in land change modeler (LCM) through MLP based on seven driving factors. Then, the land-use map for 2019 (for validation) and 2040 was simulated using the transition potential map and an agent-based approach. The ABM scenario was farmers' and urban landowners' decisions to convert undeveloped and unprotected lands to residential lands. The results showed that residential areas and pasture lands would grow by 67.96 km² and 64.63 km², and agricultural and barren lands would degrade about 84.19 km² and 47.98 km² during 2019-2040, respectively. Predicting land-use change through the integrated MLP-ABM model may be used to evaluate the effects of land-use change coming out of human decision-making.

Shifting the dynamics: implementation of locally driven, mixed-methods modelling to inform schistosomiasis control and elimination activities

INTRODUCTION

The integration of more diverse perspectives into the development of evidence for decision-making has been elusive, despite years of rhetoric to the contrary. This has led to cycles of population-based health interventions which have not delivered the promised results. The WHO most recently set a target for schistosomiasis elimination by 2030 and called for cross-cutting approaches to be driven by endemic countries themselves. The extent to which elimination is feasible within the time frame has been a subject of debate.

METHODS

Systems maps were developed through participatory modelling activities with individuals working on schistosomiasis control and elimination activities from the village through national levels in Uganda. These maps were first synthesised, then used to frame the form and content of subsequent mathematical modelling activities, and finally explicitly informed model parameter specifications for simulations, using the open-source SCHISTOX model, driven by the participants.

RESULTS

Based on the outputs of the participatory modelling, the simulation activities centred around reductions in water contact. The results of the simulations showed that mass drug administration, at either the current or target levels of coverage, combined with water contact reduction activities, achieved morbidity control in high prevalence Schistosoma mansoni settings, while both morbidity control and elimination were achieved in high prevalence S. haematobium settings within the 10-year time period.

CONCLUSION

The combination of participatory systems mapping and individual-based modelling was a rich strategy which explicitly integrated the perspectives of national and subnational policymakers and practitioners into the development of evidence. This strategy can serve as a method by which individuals who have not been traditionally included in modelling activities, and do not hold positions or work in traditional centres of power, may be heard and truly integrated into the development of evidence for decision-making in global health.

Combining social network analysis and agent-based model for enabling nature-based solution implementation: The case of Medina del Campo (Spain)

Several barriers still hamper the effective implementation of Nature-Based Solutions (NBS). Among the others, this work focuses on collaboration barriers. NBS implementation claims for effective collaboration among different decision-agents. However, ambiguity in problem framings, which is ineradicable in multi-agents' decision environments, could create collaboration barriers. This work aims to demonstrate that collaboration barriers to NBS implementation can be overcome by enhancing the network of interactions among the decision-agents. An innovative method based on the integration between Social Network Analysis and hybrid Agent-Based Model/System Dynamic Model was adopted to this aim. The analysis results were used for designing networking interventions, i.e. efforts using social network characteristics that could enhance interactions mechanisms among decision-agents. The developed method was implemented in the Medina del Campo (Spain) case study. This area is characterised by one of the most critical groundwater bodies of the Duero River Basin. This work aims at supporting the implementation of suitable NBS to stop the degradation of the groundwater status and associated ecosystem services. The activities carried out within the framework of the NAIAD project showed that, by enhancing the interaction mechanisms, ambiguity in problem frames can still yield collective actions for NBS implementation.

Evaluating the Application of the Mental Model Mapping Tool (M-Tool)

Mental models influence how individuals think and act in relation to their external environment and have been identified as leverage points to address sustainability challenges. Given the importance of mental models, a new tool to assess mental models has been developed: the Mental Model Mapping Tool (M-Tool). M-Tool was designed to have a standardized format and to be user-friendly for low literacy populations, using pictograms and audio instructions. In this paper, we evaluate M-Tool's application in two studies with Tanzanian fishers. In Study 1, we investigated M-tool's convergent validity compared to standard interviewing methods (n = 30). Study 2 investigated M-Tool's construct validity by relating mental model complexity to participants' education level (n = 185), a relationship that has been well established. The findings show that (1) mental models produced with M-Tool are more complex than mental models obtained through interviewing techniques; (2) model composition is similar across the two methods; and (3) participants with higher levels of education tend to produce more complex mental models with M-Tool than participants with lower levels of education, in line with previous research. These findings suggest that M-Tool can successfully capture mental models among diverse participants. This tool offers researchers and practitioners an instrument to map and compare perceptions of (conservation) challenges across groups.

Causal Loop Diagrams for supporting Nature Based Solutions participatory design and performance assessment

The contribution of Nature Based Solutions (NBSs) for supporting climate change adaptation and water-related risks reduction is becoming increasingly relevant for policy and decision-makers, compared to 'grey infrastructures', thanks to their capability to jointly deal with a multiplicity of societal and environmental challenges, producing several co-benefits besides limiting the impacts of water-related risks. Nevertheless, their mainstreaming is still limited by several barriers, which are often related to socio-institutional (e.g. limited cooperation and stakeholders' involvement, limited awareness about NBSs impacts) rather than to technical aspects. In this context, innovative tools for NBSs planning, design, implementation and assessment are required, along with effective processes capable of supporting stakeholders' participation. The present research aims to propose a shift in the approach to NBSs design, based on the early stakeholders' involvement in the identification, modelling and performance assessment in terms of benefits and, particularly, co-benefits production. A multi-step methodology was implemented for the purpose, combining both individual and participatory activities. Reference is made to one of the case studies of the NAIAD project, namely the Balta Potelu Pond Area (Lower Danube, Romania). Causal Loop Diagrams (CLDs) were used to describe the system in terms of causal connections and mutual influences, incorporating stakeholders' views and ideas. Inputs from both institutional (e.g. ministries and municipalities) and non-institutional stakeholders (e.g. NGOs and members of the local communities) were integrated. This allowed a comparative assessment of multiple NBSs, based on the analysis of benefits and co-benefits produced, as well as the identification of trade-offs among different stakeholders (e.g. the increase of agricultural production versus biodiversity conservation) and potential side effects. CLDs were then coupled with a Performance Matrix (a basic feature of Multi-Criteria Decision Analysis) and fuzzy logic to help decision-makers identify the most suitable NBSs for the area. The whole process was aimed at facilitating the process of NBSs selection and analysis, while considering the multiple impacts associated with their implementation.

Human Simulation and Sustainability: Ontological, Epistemological, and Ethical Reflections

This article begins with a brief outline of recent advances in the application of computer modeling to sustainability research, identifying important gaps in coverage and associated limits in methodological capability, particularly in regard to taking account of the tangled human factors that are often impediments to a sustainable future. It then describes some of the ways in which a new transdisciplinary approach within “human simulation” can contribute to the further development of sustainability modeling, more effectively addressing such human factors through its emphasis on stakeholder, policy professional, and subject matter expert participation, and its focus on constructing more realistic cognitive architectures and artificial societies. Finally, the article offers philosophical reflections on some of the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation, considered in light of the importance of human factors, including values and worldviews, in the modeling process. Based on this philosophical analysis, we encourage more explicit conversations about the value of naturalism and secularism in finding and facilitating effective and ethical strategies for sustainable development.

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded.

Improving Representation of Decision Rules in LUCC-ABM: An Example with an Elicitation of Farmers’ Decision Making for Landscape Restoration in Central Malawi

Restoring interlocking forest-agricultural landscapes—forest-agricscapes—to sustainably supply ecosystem services for socio-ecological well-being is one of Malawi’s priorities. Engaging local farmers is crucial in implementing restoration schemes. While farmers’ land-use decisions shape land-use/cover and changes (LUCC) and ecological conditions, why and how they decide to embrace restoration activities is poorly understood and neglected in forest-agricscape restoration. We analyze the nature of farmers’ restoration decisions, both individually and collectively, in Central Malawi using a mixed-method analysis. We characterize, qualitatively and quantitatively, the underlying contextual rationales, motives, benefits, and incentives. Identified decision-making rules reflect diverse and nuanced goal frames of relative importance that are featured in various combinations. We categorize the decision-making rules as: problem-solving oriented, resource/material-constrained, benefits-oriented, incentive-based, peers/leaders-influenced, knowledge/skill-dependent, altruistic-oriented, rules/norms-constrained, economic capacity-dependent, awareness-dependent, and risk averse-oriented. We link them with the corresponding vegetation- and non-vegetation-based restoration practices to depict the overall decision-making processes. Findings advance the representation of farmers’ decision rules and behavioral responses in computational agent-based modeling (ABM), through the decomposition of empirical data. The approach used can inform other modeling works attempting to better capture social actors’ decision rules. Such LUCC-ABMs are valuable for exploring spatially explicit outcomes of restoration investments by modeling such decision-making processes and policy scenarios.

Big Data Analytics for Search Engine Optimization

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.