This report sums up a literature survey of meta-analytical methods. The objective of the survey is to cover the state-of-the-art in three areas of meta-analysis where we believe there are still unsolved problems and where the choice of approach may still be contentious. These areas are the treatment of heterogeneity, the problem of publication bias and the assessment and incorporation of the quality of the individual studies.

A literature survey

Modeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: " Keep It Simple, Stupid ". But the community is divided and a new principle appeared: KIDS, " Keep It Descriptive Simple ". Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as BDI agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.

https://hal.inria.fr/hal-01363962/document

BDI agents in social simulations: a survey

The design of multiagent simulations devoted to complex systems, addresses the issue of modeling behaviors that are involved at different space, time, behavior scales, each one being relevant so as to represent a feature of the phenomenon. We propose here a generic formalismintended to represent multiple environments, endowed with their own spatiotemporal scales and with behavioral rules for the agents they contain. An environment can be nested inside any agent, which itself is situated in one or more environments. This leads to a lattice decomposition of the global system, which appears to be necessary for an accurate design of multi-scale systems. This uniform representation of entities and behaviors at each abstraction level relies upon an interaction-oriented approach for the design of agent simulations, which clearly separates agents from interactions, from the modeling to the code. We also explain the implementation of our formalism within an existing interaction-based platform.

https://hal.archives-ouvertes.fr/hal-00826401/document

An interaction-oriented model for multi-scale simulation

Agent-based models (ABMs) are increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire-Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is flexible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration of off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community.

/pdf/integrating-bdi-agents-with-agent-based-simulation-platforms-42ufvsvyl3.pdf

Integrating BDI Agents with Agent-Based Simulation Platforms

Agent-based simulations are now widely used to study complex systems. However, the problem of the agent design is still an open issue, especially for social-ecological models, where some of the agents represent human beings. In fact, designing complex agents able to act in a believable way is a difficult task, in particular when their behaviour is led by many conflicting needs and desires. A widely used way to formalise the internal architecture of such complex agents is the BDI (Belief-Desire-Intention) paradigm. This paradigm allows to design expressive and realistic agents, yet, it is rarely used in simulation context. A reason is that most agent architectures based on the BDI paradigm are complex to understand by non-computer-scientists. Moreover, they are often very time- consuming in terms of computation. In this paper, we propose a new architecture based on the BDI paradigm that copes with these two issues. In our architecture, the choice of the most relevant action by an agent is based on the belief theory. We present an application of our agent architecture to an actual model dedicated to cropping plan decision-making. This application that takes into plays thousands of farmer agents shows promising results.

/pdf/a-new-bdi-agent-architecture-based-on-the-belief-theory-vaz88ad1sr.pdf

A new BDI agent architecture based on the belief theory. Application to the modelling of cropping plan decision-making

Agent-based simulations are an increasingly popular means of exploring and understanding complex social systems. In order to be useful, these simulations must capture a range of aspects of the modeled situation, each possibly requiring distinct expertise. Moreover, different paradigms may be useful in modelling, ranging from those that use many lightweight reactive agents, to those that use cognitive agents, to those that focus on agent teams and organisational structures. There is need for an architecture which supports the development of a large simulation, through the integration of separately developed modules. This paper describes a framework and architecture which facilitates the integration of multiple agent-based simulations into a single global simulation. This architecture naturally supports distributed simulation and incremental development, which are ways of addressing the computational and conceptual complexity of such systems. In this paper we focus particularly on how to ensure proper management of simulation data that is affected by agents in different modules, at the same logical time. We also provide some preliminary performance evaluation addressing scalability, as well as a comparison of how other available systems handle the issue of shared data.

/pdf/an-architecture-for-modular-distributed-simulation-with-39zy8x4vcq.pdf

An architecture for modular distributed simulation with agent-based models

Agent Based modeling (ABM) platforms such as Repast and its predecessors are popular for developing simulations to understand complex phenomenon and interactions. Such simulations are increasingly used as support tools for policy and planning. This work takes a Belief Desire Intention (BDI) agent platform and embeds it into Repast, to support more powerful modeling of human behavior. We describe the issues faced in integrating the two paradigms, and how we addressed these issues to leverage the relevant advantages of the two approaches for real world applications.

/pdf/integrating-bdi-reasoning-into-agent-based-modeling-and-4ujtoi37w1.pdf

Integrating BDI reasoning into agent based modeling and simulation

Computer simulation is a powerful technology which could be used by the emergency management sector to improve an understanding of complex scenarios. We present two emerging simulation technologies, Agent Based Modelling and Modular simulation development, and describe how they could aid with communication, collaboration and understanding of complex emergency management scenarios.

Using modular simulation and agent based modelling to explore emergency management scenarios

Agent based simulations often model humans and increasingly it is necessary to do this at an appropriate level of complexity. It has been suggested that the Belief Desire Intention (BDI) paradigm is suitable for modeling the cognitive processes of agents representing (some of) the humans in an agent based modeling simulation. This approach models agents as having goals, and reacting to events, with high level plans, or plan types, that are gradually refined as situations unfold. This is an intuitive approach for modeling human cognitive processes. However, it is important that users can understand, verify and even contribute to the model being used. We describe a tool that can be used to explore, understand and modify, the BDI model of an agent's cognitive processes within a simulation. The tool is interactive and allows users to explore options available (and not available) at a particular agent decision point.

/pdf/user-understanding-of-cognitive-processes-in-simulation-a-d90m3i3uf5.pdf

User understanding of cognitive processes in simulation: a tool for exploring and modifying

Bushfire BLOCKS is a modular, distributed, agent-based simulation for exploring and informing bushfire response strategies. Separate independent modules capture the fire spread, evacuation of traffic and human behaviour such as the decision whether to remain at property. These modules have been developed largely independently, using paradigms and data models appropriate to their individual purpose. Modules are integrated into a single global simulation via central services for managing time advancement and access to shared variables. Execution is distributed, with modules running on different machines, whilst a custom interface enables viewing and control from one screen. The underlying architecture of this system facilitates extension of individual modules and addition of new modules, with limited alteration to other modules.

David Scerri

Papers

An architecture for modular distributed simulation with agent-based models

Integrating BDI reasoning into agent based modeling and simulation

Using modular simulation and agent based modelling to explore emergency management scenarios

User understanding of cognitive processes in simulation: a tool for exploring and modifying

Bushfire BLOCKS: a modular agent-based simulation