Theoretical and experimental studies from psychological and behavioral sciences show that heuristics and social networks play an important role in decision-making under risk. The goal of this paper is to investigate the effects of empirical social networks and different behavioral rules on farmers’ irrigation adoption under drought risk and its impacts on several macroeconomic indicators such as the rate of adaptation, water demand and regional agricultural income. We present an application of a spatial economic ABM which is able to simulate the effect of droughts on crop production, farm income and farm decision-making. The agents’ population is parameterized using survey data, including data on social networks. Four experiments are conducted combining two climate scenarios with two behavioral scenarios (maximizers vs. heuristic-based agents). The results show that the adoption process follows a different path in the scenario with heuristic-based farmers. The adoption of irrigation is slower in the short run due to reliance on information from social networks and farmers’ uncertainty regarding drought events. This results in agricultural income loss and a lower water demand in the short run compared to the scenario with maximizing agents.

Climate change projections show that periods of droughts are likely to increase, causing decreasing water availability, salinization, and consequently farm income loss in the south-west Netherlands. Adaptation is the key to decrease a farmer’s drought vulnerability and to secure the agricultural sector’s performance at the aggregate level. Possible adaptation strategies include responses at the field scale, farm-level measures and joint adaptation measures. Using the results of a recent survey, we explore farmers’ adaptive behaviour to drought. We give detailed insight into the influence of risk appraisal and coping appraisal factors on the current level of farmers’ adaptation motivation and the adoption of three types of adaptive responses. Our findings show that behavioural factors make a significant contribution to explain the actual level of farmers’ adaptation motivation. Furthermore, we find that components of threat and coping appraisal influence adoption decisions differently across three types of drought adaptation measures.

Drought-induced water shortage and salinization are a global threat to agricultural production. With climate change, drought risk is expected to increase as drought events are assumed to occur more frequently and to become more severe. The agricultural sector's adaptive capacity largely depends on farmers' drought risk perceptions. Understanding the formation of farmers' drought risk perceptions is a prerequisite to designing effective and efficient public drought risk management strategies. Various strands of literature point at different factors shaping individual risk perceptions. Economic theory points at objective risk variables, whereas psychology and sociology identify subjective risk variables. This study investigates and compares the contribution of objective and subjective factors in explaining farmers' drought risk perception by means of survey data analysis. Data on risk perceptions, farm characteristics, and various other personality traits were collected from farmers located in the southwest Netherlands. From comparing the explanatory power of objective and subjective risk factors in separate models and a full model of risk perception, it can be concluded that farmers' risk perceptions are shaped by both rational and emotional factors. In a full risk perception model, being located in an area with external water supply, owning fields with salinization issues, cultivating drought-/salt-sensitive crops, farm revenue, drought risk experience, and perceived control are significant explanatory variables of farmers' drought risk perceptions.

Adaptation to climate change might not always occur, with potentially catastrophic results. Success depends on coordinated actions at both governmental and individual levels (public and private adaptation). Even for a "wet" country like the Netherlands, climate change projections show that the frequency and severity of droughts are likely to increase. Freshwater is an important factor for agricultural production. A deficit causes damage to crop production and consequently to a loss of income. Adaptation is the key to decrease farmers' vulnerability at the micro level and the sector's vulnerability at the macro level. Individual adaptation decision-making is determined by the behavior of economic agents and social interaction among them. This can be best studied with agentbased modelling. Given the uncertainty about future weather conditions and the costs and effectiveness of adaptation strategies, a farmer in the model uses a cognitive process (or heuristic) to make adaptation decisions. In this process, he can rely on his experiences and on information from interactions within his social network. Interaction leads to the spread of information and knowledge that causes learning. Learning changes the conditions for individual adaptation decisionmaking. All these interactions cause emergent phenomena: The diffusion of adaptation strategies and a change of drought vulnerability of the agricultural sector. In this paper, we present a conceptual model and the first implementation of an agent-based model. The aim is to study the role of interaction in a farmer's social network on adaptation decisions and on the diffusion of adaptation strategies and vulnerability of the agricultural sector. Micro-level survey data will be used to parameterize agents' behavioral and interaction rules at a later stage. This knowledge is necessary for the successful design of public adaptation strategies, since governmental adaptation actions need to be fine-tuned to private adaptation behavior.

Dutch coastal land markets are characterized by high amenity values but are threatened by potential coastal hazards, leading to high potential damage costs from flooding. Yet, Dutch residents generally perceive low or no flood risk. Using an agent-based land market model and Dutch survey data on risk perceptions and location preferences, this paper explores the patterns of land development and land rents produced by buyers with low, highly skewed risk perceptions. We find that, compared to representative agent and uniform risk perception models, the skewed risk perception distribution produces substantially more, high-valued development in risky coastal zones, potentially creating economically significant risks triggered by the current Dutch flood protection policy.

Coastal flood risk is defined as a product of probability of event and its effect, measured in terms of damage. The paper is focused on coastal management strategies aimed to decrease risk by decreasing potential damage. We review socio-economic literature to show that total flood damage depends on individual location choices in the housing market and on individual flood risk awareness. Low flood risk awareness leads to inefficient spatial developments and increased flood risk. We show that personal experience, risk communication, financial instruments like insurance from flooding and technical instruments like building on high elevations, are factors that increase individual risk awareness. Evidence that these factors indeed affect housing prices and land use patterns is provided. We discuss proactive instruments that can be used in coastal zone management in the Netherlands to increase individual risk awareness. We argue that policy-makers may create incentives giving individuals a possibility to make location choices that lead to less total flood risk in the coastal zone area.

This paper presents an agent-based model of a land market, which is used to explore the effects of land taxes on the land use in a coastal zone. The model simulates the emergence of land prices and urban land patterns from bottom-up via interactions of individual agents in a land market. A series of model experiments helps visualize and explore how economic incentives in a land market may influence the spatial distribution of land prices and urban developments, either leaving space for coastal ecosystems or not. We demonstrate that economic incentives do affect urban form and pattern, land prices and welfare measures. However, they may not always be sufficient to reduce the pressure on coastal ecosystems. Our results show that preservation of ecosystems may involve difficult trade-offs between economic and ecological priorities, as well as between healthy ecosystems and social equity. We also show how conventional economic modelling based on a representative agent, which is usually employed by policy makers, overestimates both environmental benefits and economic costs associated with the tax meant to preserve coastal ecosystems.

We present a new bilateral agent-based land market model, which moves beyond previous work by explicitly modeling behavioral drivers of land-market transactions on both the buyer and seller sides; formation of bid prices (of buyers) and ask prices (of sellers); and the relative division of the gains from trade from the market transactions. We analyze model output using a series of macro-scale economic and landscape pattern measures, including land rent gradients estimated using simple regression models. We first demonstrate that our model replicates relevant theoretical results of the traditional Alonso/Von Thünen model (structural validation). We then explore how urban morphology and land rents change as the relative market power of buyers and sellers changes (i.e., we move from a 'sellers' market' to a 'buyers' market'). We demonstrate that these strategic price dynamics have differential effects on land rents, but both lead to increased urban expansion.

Heterogeneity in both the spatial environment and economic agents is a crucial driver of land market dynamics. We present an agent-based land market model where land from agriculture use is transferred into urban. The model combines the microeconomic demand, supply, and bidding foundations of spatial economics models with the spatial heterogeneity of spatial econometric models in a single methodological platform. Heterogeneous agents exchange heterogeneous spatial goods via simulated bilateral market interactions. We model a coastal city where both coastal amenities and flooding or erosion disamenities drive land market outcomes, facilitating separate analysis of the effects of each driver on land rents and land development patterns. We also analyze the implications of homogeneous versus heterogeneous but unbiased flood risk perceptions. Since buyers with low risk perceptions drive market outcomes, spatial development under heterogeneous risk perceptions differs qualitatively, with more expansion into risky areas. Our results highlight the shortcomings of policy models based on representative agent assumptions and the importance of including agent-level data in empirical modeling.

This paper aims to understand the effects of biases in individual flood risk perception on aggregated land use patterns and their implications for macro policy. We develop a spatially explicit land market model and param-eterize individual risk perceptions with data from a survey held in the Nether-lands in 2008. Two sets of experiments are presented. A model with heteroge-neous agents produces qualitatively different results compared to a model with homogeneous agents. Individuals with low flood risk perception drive urban developments into the economically inefficient zone and leading to the increas-ing potential damage.

This paper presents an agent-based model of a land market (ALMA-C) to simulate the emergence of land prices and urban land patterns from bottom-up. Our model mimics individual decisions to buy and to sell land depending on economic, sociological and political factors as well as on the characteristics of the spatial environment. To this we add ecological and environmental considerations and focus on the question of how individual land use decisions can be affected to reduce the pressure on the coastal zone ecosystem functions. A series of model experiments helps visualize and explore how economic incentives at a land market can influence the spatial distribution of activities and land prices in a coastal zone. We demonstrate that economic incentives do affect urban form and pattern, land prices and welfare measures. However, they may not always be sufficient to reduce the pressure on coastal zone ecosystems.

We construct a spatially explicit agent-based model of a bilateral land market. Heterogeneous agents form their bid and ask prices for land based on the utility that they obtain from a certain location (house/land) and based on the state of the market (an excess of demand or supply). We underline the distinction between bid /ask price and individual willingness to pay/to accept and show that variations between them that reflect market conditions can influence land prices. Agents sort among locations with respect to distance from the city center and environmental spatial externalities. Aggregated outcomes such as land patterns and land prices are produced by the model. The basic model of buyers and sellers trading land in the urban area produces results identical to the monocentric urban model. However, more complex dynamics appears when environmental amenities and market-adjustment variable influence the formation of land prices.

Economic growth causes growing urbanization, extension of tourist sector, infrastructure and change of natural landscape. These processes of land use change attract even more attention if they take place in coastal zone area. In that case not only the efficient allocation and preservation of natural area, but also reduction of potential damage from flooding is important. Driven forces of land use at macro and micro levels should be taken into account. This paper presents an agent based model (ABM), which is designed to simulate land use change in coastal zone area based of human behaviour. The aim is to understand motives, types of connections and interactions between different actors and natural environment in order to get a feeling how different policy options and natural conditions might affect land use configuration. Microeconomic motives of land use decisions are in the focus of the research. Individual land use decisions are guided by economic and geomorphologic conditions, spatial planning and coastal protection policy. Each location choice is done according to a set of defined rules and land attributes. Space is represented as a grid of cells. Self-interested economic agents interact with each other trying to benefit from a certain type of land-use. We introduce the perception of risk of flooding in the model of land use as an innovative aspect of ABM simulations for water management problems. Based on decisions of spatially distributed individual economic agents operating in a policy framework, the model produces aggregated land-use patterns as an outcome. Understanding the factors that affect land use decisions will help policy makers design incentives to achieve policy objectives in coastal zone area. The proposed ABM will be applied to a study area in the province of North Holland in the Netherlands.