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.

Implications of adopting a biodiversity-based vulnerability index versus a shoreline environmental sensitivity index on management and policy planning along coastal areas.

In this study, a multi-criteria index was developed to assess anthropogenic stressors along the Mediterranean coastline. The index aimed at geo-locating pollution hotspots for informed decision making related to coastal zone management. The index was integrated in a Geographical Information System based geodatabase implemented at several pilot areas along the Northern (Italy and France), Eastern (Lebanon), and Southern (Tunisia) Mediterranean coastlines. The generated stressor maps were coupled with a biodiversity richness index and an environmental sensitivity index to produce vulnerability maps that can form the basis for prioritizing management and mitigation interventions towards the identification of pollution hotspots and the promotion of sustainable coastal zone management. The results identified significant differences between the two assessment methods, which can bias prioritization in decision making and policy planning depending on stakeholders' interests. The discrepancies emphasize the need for transparency and understanding of the underlying foundations behind vulnerability indices and mapping development.

Risk assessment of oil spills along the Mediterranean coast: A sensitivity analysis of the choice of hazard quantification.

Oil pollution in the Mediterranean represents a serious threat to the coastal environment. Quantifying the risks associated with a potential spill is often based on results generated from oil spill models. In this study, MEDSLIK-II, an EU funded and endorsed oil spill model, is used to assess potential oil spill scenarios at four pilot areas located along the northern, eastern, and southern Mediterranean shoreline, providing a wide range of spill conditions and coastal geomorphological characteristics. Oil spill risk assessment at the four pilot areas was quantified as a function of three oil pollution metrics that include the susceptibility of oiling per beach segment, the average volume of oiling expected in the event of beaching, and the average oil beaching time. The results show that while the three pollution metrics tend to agree in their hazard characterization when the shoreline morphology is simple, considerable differences in the quantification of the associated hazard is possible under complex coastal morphologies. These differences proved to greatly alter the evaluation of environmental risks. An integrative hazard index is proposed that encompasses the three simulated pollution metrics. The index promises to shed light on oil spill hazards that can be universally applied across the Mediterranean basin by integrating it with the unified oil spill risk assessment tool developed by the Regional Marine Pollution Emergency Response Centre for the Mediterranean (REMPEC).

The gambler's ruin problem, genetic algorithms, and the sizing of populations.

This paper presents a model to predict the convergence quality of genetic algorithms based on the size of the population. The model is based on an analogy between selection in GAs and one-dimensional random walks. Using the solution to a classic random walk problem-the gambler's ruin-the model naturally incorporates previous knowledge about the initial supply of building blocks (BBs) and correct selection of the best BB over its desired quality of the solution, as well as the problem size and difficulty. The accuracy of the model is verified with experiments using additively decomposable functions of varying difficulty. The paper demonstrates how to adjust the model to account for noise present in the fitness evaluation and for different tournament sizes.