Interaction metric of emergent behaviors in agent-based simulation

Chan, Catherine S.

doi:10.1109/wsc.2011.6147763

Cited by 33 publications

(34 citation statements)

References 21 publications

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“…A mathematical approach for representing strong emergent properties of a system has been proposed by measuring entropy at different levels of abstraction [Bar-Yam 2004]. Chan [Chan 2011] acknowledges the importance of interactions between agents in creating emergence in a system. Here, detection of emergence is facilitated by monitoring a time-series of the number of interactions and state changes across all agents in the system.…”

Section: Related Workmentioning

confidence: 99%

Decentralised Detection of Emergence in Complex Adaptive Systems

O'Toole

Nallur

Clarke

2017

ACM Trans. Auton. Adapt. Syst.

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This paper describes Decentralised Emergence Detection (DETect), a novel distributed algorithm that enables agents to collaboratively detect emergent events in Complex Adaptive Systems (CAS). Nondeterministic interactions between agents in CAS can give rise to emergent behaviour or properties at the system level. The nature, timing and consequence of emergence is unpredictable and may be harmful to the system or individual agents. DETect relies on the feedback that occurs from the system level (macro) to the agent level (micro) when emergence occurs. This feedback constrains agents at the micro-level, and results in changes occurring in the relationship between an agent and its environment. DETect uses statistical methods to automatically select the properties of the agent and environment to monitor, and tracks the relationship between these properties over time. When a significant change is detected, the algorithm uses distributed consensus to determine if a sufficient number of agents have simultaneously experienced a similar change. On agreement of emergence, DETect raises an event, which its agent or other interested observers can use to act appropriately. The approach is evaluated using a multi-agent case study.

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Section: Related Workmentioning

confidence: 99%

Decentralised Detection of Emergence in Complex Adaptive Systems

O'Toole

Nallur

Clarke

2017

ACM Trans. Auton. Adapt. Syst.

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“…The interaction data is described in Chan (2011). This data is maintained and updated through the Simulator Exchange.…”

Section: Figure 34 Predator Prey Agent Metricsmentioning

confidence: 99%

“…First, we allow each model (agent) to identify the data it wants to collect (through the Experimental frame) and alert the Metrics Collector when the values have changed using the publish/subscribe architectural style. Second, we begin with the method described in (Chan W. K., 2011) for identifying emergent behavior, which increases a counter containing the number of interactions among agents whenever an agent has to interact with another agent based on some action that the agent has to perform. Chan then graphs these interaction metrics for each run and asserts that the interaction metric deviates from a normal curve (for a particular run) when emergent behaviors arise.…”

Section: Summary Of Significant Findingsmentioning

confidence: 99%

Identifying and Quantifying Emergent Behavior Through System of Systems Modeling and Simulation

Cummings¹

2015

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington ABSTRACT (maximum 200 words)The U.S. Department of Defense builds many weapons systems as Systems of Systems (SoSs). Operational testing is used to test capabilities in these SoSs, but testing all configurations of the SoSs may not be possible due to cost and the environment. Modeling and simulation (M&S) is an alternative to operationally testing all functionality and interfaces in these SoSs. An inherent deficiency of existing M&S approaches, however, lies in the emergent behavior that occurs as a result of interactions among the component systems. Applying M&S to individual components cannot detect this emergent behavior, but not detecting emergent behavior of an SoS as a whole diminishes its testing process. This dissertation develops a software architecture for an M&S framework, based on Ziegler's theory, that makes the identification and quantification of emergent behavior possible in an SoS M&S by providing a collector of SoS metrics. This software architecture has a great advantage over the current approaches because it supports swappable and configurable components to make simulation possible over different models and parameters without costly reprogramming. This dissertation also develops an improved process for the analyst to identify and quantify emergent behavior in an SoS M&S. SUBJECT TERMS

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“…The boids model [33] captures the motion of bird flocking and is a seminal example for studying emergence [9]. At the macro level, a group of birds tends to move in a Vlike formation, which has aerodynamic advantages, obstacle avoidance, and predator protection, regardless of the initial positions of the birds.…”

Section: Example: Flock Of Birds Modelmentioning

confidence: 99%

“…Emergence study provides great potential to understand the interactions of agents and their environment [9,10], and to explain how natural systems work [35]. Furthermore, it is important to know whether emergent properties are beneficial or harmful so that we can exploit the positive impacts of the beneficial properties and minimize, or even prevent negative consequences due to the harmful properties.…”

Section: Introductionmentioning

confidence: 99%

Formalization of emergence in multi-agent systems

Teo

Luong

Szabo

2013

Proceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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Emergence is a distinguishing feature in systems, especially when complexity grows with the number of components, interactions, and connectivity. There is immense interest in emergence, and a plethora of definitions from philosophy to sciences. Despite this, there is a lack of consensus on the definition of emergence and this hinders the development of a formal approach to understand and predict emergent behavior in multi-agent systems. This paper proposes a grammarbased set-theoretic approach to formalize and verify the existence and extent of emergence without prior knowledge or definition of emergent properties. Our approach is based on weak (basic) emergence that is both generated and autonomous from the underlying agents. In contrast with current work, our approach has two main advantages. By focusing only on system interactions of interest and feasible combinations of individual agent behavior, state-space explosion is reduced. In formalizing emergence, our extended grammar is designed to model agents of diverse types, mobile agents, and open systems. Theoretical and experimental studies using the boids model demonstrate the complexity of our formal approach.

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Interaction metric of emergent behaviors in agent-based simulation

Cited by 33 publications

References 21 publications

Decentralised Detection of Emergence in Complex Adaptive Systems

Decentralised Detection of Emergence in Complex Adaptive Systems

Identifying and Quantifying Emergent Behavior Through System of Systems Modeling and Simulation

Formalization of emergence in multi-agent systems

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