A actor-based architecture for customizing and controlling agent ensembles

Jamali, Nadeem; Thati, Prasannaa; Agha, Gul

doi:10.1109/5254.757630

Cited by 32 publications

(13 citation statements)

References 10 publications

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“…In such situations, the inputs from the individual agents need to be synchronised, examined and shared before the final feedback or suggestion to the investor is provided (Jamali et al 1999). In the next section, we propose a DSS architecture to illustrate a trading scenario involving potential house-money effect.…”

Section: Conative Debiasing Agentsmentioning

confidence: 99%

An agent-based debiasing framework for investment decision-support systems

Bhandari¹,

Hassanein²

2012

Behaviour & Information Technology

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Though researchers agree on the role of psychological forces on individuals' decision-making and emphasise the need for developing decision-support systems (DSS) that make individuals aware of these forces, a framework that can guide us in building such systems is still non-existent. In this article, we attempt to bridge this gap by proposing an agent-based debiasing framework for developing investment DSS. Identifying the primary characteristics of major biases influencing investment decisions through a thorough literature review, we propose a taxonomy to categorise them as cognitive, affective or conative. Cognitive biases are information-processing biases. Affective biases involve general moods and emotions. Conative biases are relatively stable personality traits such as overconfidence and inertia. We then outline debiasing strategies for each of these bias categories and identify decision-support characteristics necessary in software agents to carry out the appropriate debiasing tasks. An agentbased DSS architecture is then proposed, and a detailed trading example triggering a sample bias detection and debiasing algorithm is discussed to demonstrate the feasibility of the proposed system.

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Section: Conative Debiasing Agentsmentioning

confidence: 99%

An agent-based debiasing framework for investment decision-support systems

Bhandari¹,

Hassanein²

2012

Behaviour & Information Technology

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“…Agents can be represented as actors, which consist of interface, state and methods. Actors are self-contained, interactive, autonomous entities of a computing system which communicate through asynchronous message passing [17]. Essentially an agent should have two primary capabilities, i.e., autonomy and social ability.…”

Section: Agent-based Modeling and Simulation (Abms)mentioning

confidence: 99%

Agent-based modeling and simulation of Complex Distributed Systems

Tianfield

Jiang

2008

2008 7th World Congress on Intelligent Control and Automation

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The complexity of Complex Distributed Systems (CODS) is essentially attributed to the inherent autonomy of the sub-systems and the dynamic intricate interactions among the sub-systems. Since Agent-Based Modeling and Simulation (ABMS) is good at modeling and simulating such systems where there are a large number of heterogeneous entities behaving independently, ABMS is well suitable for CODS. This paper identifies ABMS's main differences compared to conventional simulation approaches, and analyzes briefly some important ABMS platforms relevant to CODS. Finally, ABMS of biological systems is discussed as typical of CODS.Index Terms -agent-based modeling, agent-based simulation, complex adaptive system, complex distributed system, multi-agent system.

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“…As such, it must be more like an animal: mobile and able to forage for resources, learn from the environment, evolve, and provide services. One can imagine cyberorganisms [2] roaming the pervasive cyberspace, gaining "wealth" by providing services, negotiating with computers to sense the environment, and computing, actuating, and learning (self-modifying) from the results of their actions. This is not to argue that software will be entirely analogous to biological hardware-even if we were to create biological computers, as we will eventually.…”

Section: Freed From the Temporal Constraints Of Hardware Software Comentioning

confidence: 99%