Complexity and information: Measuring emergence, self‐organization, and homeostasis at multiple scales

Gershenson, Carlos; Fernández, Nelson

doi:10.1002/cplx.21424

Cited by 149 publications

(137 citation statements)

References 75 publications

(78 reference statements)

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“…Our measure of complexity C reconciles these two opposing views, as a balance between order and chaos is maximal with a high C. Dynamical systems such, cellular automata [51], random Boolean networks [47,52,53], and Ising models [54] have a maximal C in the region their dynamics are considered most complex [41]. Since living systems also require a balance between adaptivity (E) and stability (S) [47,55], C can be used to characterize living systems, especially when comparing their C with that of their environment [27].…”

Section: Methodssupporting

confidence: 54%

“…In previous work [41], we showed that elementary cellular automata with gliders have higher E than those which produce static patterns (ordered dynamics). Also, gliders can be seen as "more information" in Shannon's sense than static patterns.…”

Section: Methodsmentioning

confidence: 88%

“…We recently proposed measures of emergence, self-organization, and complexity based on information theory in [41] and refined them and based them on axioms in [27]. These axioms are included in Appendix A.1 for quick reference.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Measuring the Complexity of Self-Organizing Traffic Lights

Zubillaga

Cruz

Aguilar

et al. 2014

Entropy

Self Cite

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We apply measures of complexity, emergence, and self-organization to an urban traffic model for comparing a traditional traffic-light coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only is traffic a non-stationary problem, requiring controllers to adapt constantly; controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures and extending Ashby's law of requisite variety, we can say that the self-organizing method achieves an adaptability level comparable to that of a living system.

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Section: Methodssupporting

confidence: 54%

Section: Methodsmentioning

confidence: 88%

See 1 more Smart Citation

Measuring the Complexity of Self-Organizing Traffic Lights

Zubillaga

Cruz

Aguilar

et al. 2014

Entropy

Self Cite

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“…For instance, we are considering applying this to mazes (in comparison with [62]), the matching pennies game (in relation to [37] and other normal-form games in game theory) or generalisations of cellular automata, such as Random Boolean Networks [26], in a setting that would highly resemble the one introduced in [33].…”

Section: Discussionmentioning

confidence: 99%

On environment difficulty and discriminating power

Hernández-Orallo

2014

Auton Agent Multi-Agent Syst

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This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multi-agent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multi-agent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.

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“…If the organism's representation of the environment E has algorithmic complexity x, E should have a complexity of no less than x. We have advanced the claim that the algorithmic complexity of biological systems follows the algorithmic complexity of the environment (a similar approach to a related biological question is presented in [42]). …”

Section: Discussionmentioning

confidence: 99%

Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments

Zenil

Gershenson

Marshall

et al. 2012

Entropy

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In evolutionary biology, attention to the relationship between stochastic organisms and their stochastic environments has leaned towards the adaptability and learning capabilities of the organisms rather than toward the properties of the environment. This article is devoted to the algorithmic aspects of the environment and its interaction with living organisms. We ask whether one may use the fact of the existence of life to establish how far nature is removed from algorithmic randomness. The paper uses a novel approach to behavioral evolutionary questions, using tools drawn from information theory, algorithmic complexity and the thermodynamics of computation to support an intuitive assumption about the near optimal structure of a physical environment that would prove conducive to the evolution and survival of organisms, and sketches the potential of these tools, at present alien to biology, that could be used in the future to address different and deeper questions. We contribute to the discussion of the algorithmic structure of natural environments and provide statistical and computational arguments for the intuitive claim that living systems would not be able to survive in completely unpredictable environments, even if adaptable and equipped with storage and learning capabilities by natural selection (brain memory or DNA).

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Complexity and information: Measuring emergence, self‐organization, and homeostasis at multiple scales

Cited by 149 publications

References 75 publications

Measuring the Complexity of Self-Organizing Traffic Lights

Measuring the Complexity of Self-Organizing Traffic Lights

On environment difficulty and discriminating power

Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments

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