Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning

Computational models have been used to investigate farmers’ decision outcomes, yet classical economics assumptions prevail, while learning processes and adaptive behaviour are overlooked. This paper advances the conceptualisation, modelling and understanding of learning-by-doing and social learning, two key processes in adaptive (co-)management literature. We expand a pre-existing agent-based model (ABM) of an agricultural social-ecological system, RAGE (Dressler et al., 2018). We endow human agents with learning-by-doing and social learning capabilities, and we study the impact of their learning strategies on economic, ecological and social outcomes. Methodologically, we contribute to an under-explored area of modelling farmers’ behaviour. Results show that agents who employ learning better match their decisions to the ecological conditions than those who do not. Imitating the learning type of successful agents further improves outcomes. Different learning processes are suited to different goals. We report on conditions under which learning-by-doing becomes dominant in a population with mixed learning approaches

Learning Extension - RAGE RAngeland Grazing Model 1.0.0

This is an extension of the original RAGE model (Dressler et al. 2018), where we add learning capabilities to agents, specifically learning-by-doing and social learning (two processes central to adaptive (co-)management). The extension module is applied to smallholder farmers’ decision-making - here, a pasture (patch) is the private property of the household (agent) placed on it and there is no movement of the households. Households observe the state of the pasture and their neighrbours to make decisions on how many livestock to place on their pasture every year. Three new behavioural types are created (which cannot be combined with the original ones): E-RO (baseline behaviour), E-LBD (learning-by-doing) and E-RO-SL1 (social learning). Similarly to the original model, these three types can be compared regarding long-term social-ecological performance. In addition, a global strategy switching option (corresponding to double-loop learning) allows users to study how behavioural strategies diffuse in a heterogeneous population of learning and non-learning agents. An important modification of the original model is that extension agents are heterogeneous in how they deal with uncertainty. This is represented by an agent property, called the r-parameter (household-risk-att in the code). The r-parameter is catch-all for various factors that form an agent’s disposition to act in a certain way, such as: uncertainty in the sensing (partial observability of the resource system), noise in the information received, or an inherent characteristic of the agent, for instance, their risk attitude

Simulation of migration and demographic processes using FLAME GPU

This article presents an approach to modeling migration and demographic processes using a framework designed for large-scale agent-based modeling – FLAME GPU. This approach is based on the previously developed simulation model of interaction between two communities: migrants and natives that is implemented in the AnyLogic simulation software. The model has had a low dimensionality of the discrete space representing the operating environment of the agent populations and a deterministic decision-making system of each agent. At the same time, the presence of multiple interactions between agents and transitions between their states determines a high computational complexity of such a model. The use of FLAME GPU makes it possible to conduct extensive simulation experiments with the model, mainly due to the parallelization of computational processes at the level of each agent, as well as the implementation of the mechanism of multiple computations using Monte Carlo techniques. The developed framework is used to study the impact of the most important parameters of the model (e.g., rate of migration, governmental expenditures on integration, frequency of creation of new workplaces, etc.) on the key outputs of the modeled socio-economic system (in particular, population size, share of migrants, number of assimilated migrants, GDP growth rate, etc.). The proposed approach can be used to develop decision-making systems for planning the hiring of new employees based on the forecast dynamics of migration and demographic processes

Aggregated Agent-Based Simulation Model of Migration Flows of the European Union Countries

This article presents the aggregated agent-based simulation model of migration flows of the European Union (EU) countries, implemented in the AnyLogic system, created in the form of an extended “gravity model”, according to which individual decisions by agent-migrants are based on the integrated assessment of socio-economic, geographical and other differentiation of the respective countries. At the same time, some factors ‘attract’ migrants, while others ‘repel’. A distinctive feature of the model is the differentiation of migration flows by the categories of migrants with the release of various influencing factors that reflect the individual preferences of agent-migrants regarding to the agents-countries (EU members). At the same time, there are multiple control parameters that affect the distribution of migration flows between the EU countries, in particular, migration quotas, unemployment benefits, minimum wages, etc. The most important bicriterial optimization task of EU countries for choosing rational migration and economic policies based on maximizing integral GDP and minimizing the total number of migrants has been formulated. The issue is aimed to control parameters that affect the structure of migration flows and labor resources, as well as their maintenance costs. For the first time, an expanded gravitational model has been proposed and studied, describing dynamic migration flows with the release of multiple factors that differentiate the attractiveness of EU countries for various groups of migrants, for example, internal migrants, economic migrants, refugees, etc

Agent-based modeling of social and economic impacts of migration under the government regulated employment [in Russian]

This article presents an approach to modelling the socio-economic impacts of migration using an agent-based model (ABM) of interactions between migrants and natives. The model also accounts for a regulatory function of government which is the centralized creation of new workplaces that differ in the level of ‘technological return’ (i.e. the labour productivity that depends on the sectoral belonging of the formed workplaces). The proposed approach is based on the previously developed model of interactions between migrants and native individuals. It is focused on studying the socio-economic impacts of migration in the system with a more complex regulatory function of the government, which creates low-technological and high-technological workplaces that are attractive for migrants and natives, respectively. The agent-government has two possible strategies of workplace creation: cluster-based workplace creation in areas with high concentration of migrants and natives and creation of uniform workplaces aimed at increasing multi-particle interactions between agents of different types, and reducing the level of population segregation. This study also investigates the processes of assimilation, which are subject to the level of segregation of the studied communities, public investment in education and integration, etc. The proposed model also considers the influence of various control parameters, in particular, the influence of the agents’ tolerance level on their location choice in a boundary neighbourhood, the influence of the agents’ education level on the job search area dimension, and other important characteristics reflecting the behavioural features of members of the studied communities. Socio-economic impacts of migration are studied under various scenario conditions, which include different patterns of agents’ behaviour belonging to the considered communities, the rate of new migrants’ inflow, the amount of government education expenditures, etc

Agent-based modelling of population dynamics of two interacting social communities: migrants and natives

This article presents a new agent-based approach to modeling migration and demographic processes based on computer simulation of the population dynamics of two interacting communities: migrants and native people implementing different decision-making strategies. The approach proposed in the article is based on the well-known model of interaction between ‘nomads’ and ‘plowmen’ and focused on studying the behavior of societies with more complex behavior patterns than in the original model: native people and migrants, as well as their impact on social and economic and environmental systems. Moreover, members of both communities (societies), i.e. agent-migrants (that can be considered as ‘nomads’) and agent–native people (that can be considered as ‘plowmen’) reproduce the resources (job places) necessary to increase personal welfare and realize the opportunities for marriage and childbirth. Agent-migrants create resources with the lowest level of return, such as ‘low-technological job places’ and agent–native people reproduce ‘high-technological job places’ that provide a greater contribution to the level of personal welfare and economic growth in a common. The total number of such job placements is restricted by the spatial and demographic characteristics of the system. The suggested model takes into account the influence of many parameters, in particular, the life expectancy, the share of new migrants in previously immigrants, minimum levels of personal welfare and other important characteristics that reflect the behavior of members of the studied communities. At the same time, the effect of such parameters on migration and demographic processes, and the macroeconomic and environmental characteristics associated with them are studied

Program Packages Method for Solution of a Linear Terminal Control Problem with Incomplete Information

Closed-loop terminal control problem for the controlled linear dynamical system is considered in case of incomplete information about the actual initial state of this system. It is assumed, though, that the finite set containing the actual initial state is known. The program packages method is applied for obtaining solution of the problem. The package terminal problem is formulated and is proven to be equivalent to the closed-loop terminal control problem. The package terminal problem is further reduced to a finite-dimensional open-loop terminal control problem which can be solved using a numerical algorithm. Such an algorithm is provided in detail and applied to an illustrative example