# Parallel Agent-Based Framework for Analyzing Urban Agricultural Supply Chains
## 2 Related Work
This work applies social network analysis to study human interactions, where agent relationships are modeled as graphs. Additionally, the authors incorporated a Bayesian model to estimate farmers' risk aversion throughout the simulation. The model was trained using survey data specifically collected for the project. Although the use of LCA is relatively uncommon in the reviewed literature, the study does not consider risk factors such as frosts, droughs, or pests, nor does it extend the analysis beyond the farmers themselves. The work in [18] presents a scalable and dynamic agent-based model with environmental boundaries. Unlike other studies, the main actors are not farmers but firms. In this study, the agents do not attempt to optimize a utility function but instead adapt their decision routines, considering social and spatial proximities between agents. The lands are modeled as grids, where farmers can decide to cultivate, deforest, or abandon areas. Firms accumulate the products generated by farmers, which are then traded in the market. However, consumers are not modeled as agents. At each simulation step, the continuity of each firm is evaluated based on its profits at the end of the period. Only theoretical cases were considered. The work in [19] presents an agent-based modeling framework to predict farmer behavior. The study examines the reactions and behaviors of farmers to changes in climatic factors due to global warming. Different decision functions for farmers were evaluated, such as economic, social, or mixed. The authors conclude that the economic function was the least aligned with the expected outcome. Sixty-eight farms and 23 farmers along the eastern Mediterranean coast were considered as a case study. A simulation horizon of 15 years was used, with annual time steps. The work in [20] presents a literature review on the modeling of decision-making processes of European farmers. In particular, they focus on agent-based models (ABMs). In this context, an agent is considered any entity capable of perceiving its environment through sensors and acting in response to this perception [21]. Under this scheme, each system is modeled as a set of autonomous entities that perceive, make decisions, and interact with the environment and other entities. According to [20], ABMs have grown in popularity in the last years. The MP-MAS framework [22] uses dynamic models of interaction and decision-making with dynamic environmental models, particularly with water flow and changes in soil fertility. However, it does not consider environmental risk factors. The work in [23] proposes a spatial ABM for the evaluation of agricultural policies in relation to the diffusion of innovations and changes in resource use. The simulation is based on empirical data collected in Chile and other regions. In this particular case, the study is conducted in the context of the Mercosur treaty. Almost a decade later, [PERSON] et al. [24] used MP-MAS to study a reservoir project in Ancoa. This project aimed to build a reservoir to store water during the winter period and distribute it during the summer months. This would allow for better planning of the region's water resources. The same software is used to study human-environment interaction using ABMs. Tests with data collected in Chile and other regions are presented to study the impact of agricultural policies and the introduction of technologies. However, the MP-MAS and its associated models are not suitable for simulating scenarios where agents make collective decisions [25]. The work in [26] presents an environmental and economic assessment model that utilizes an agent-based approach combined with life cycle thinking methods to simulate bioenergy derived from crop residues. The authors identify farming income, age, farm size, and crop types as the primary factors affecting the adoption of this bioenergy. The work in [27] aims to evaluate the level of vulnerability of the strawberry agricultural system in Mexico.