Autonomy: An information theoretic perspective

Bertschinger, Nils; Olbrich, Eckehard; Ay, Nihat; Jost, Jürgen

doi:10.1016/j.biosystems.2007.05.018

Cited by 81 publications

(123 citation statements)

References 13 publications

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“…When studying gene regulation in systems biology, S could be the target gene, and one might ask for synergy between transcription factors X and Y [2]. For the behavior of an autonomous system S, one could ask to which extent it is influenced by its own state history X or the environment Y [3].…”

Section: Introductionmentioning

confidence: 99%

Information Decomposition and Synergy

Olbrich

Bertschinger

Rauh

2015

Entropy

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Abstract:Recently, a series of papers addressed the problem of decomposing the information of two random variables into shared information, unique information and synergistic information. Several measures were proposed, although still no consensus has been reached. Here, we compare these proposals with an older approach to define synergistic information based on the projections on exponential families containing only up to k-th order interactions. We show that these measures are not compatible with a decomposition into unique, shared and synergistic information if one requires that all terms are always non-negative (local positivity). We illustrate the difference between the two measures for multivariate Gaussians.

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Section: Introductionmentioning

confidence: 99%

Information Decomposition and Synergy

Olbrich

Bertschinger

Rauh

2015

Entropy

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“…The conditional entropy HpM t`1 |E t q measures the uncertainty about the next agent state when the current environment state is known. It has been proposed as part of an autonomy measure as a measure of non-heteronomy in Bertschinger et al (2008). Non-heteronomy means that the agent is not determined by the history of the environment.…”

Section: Entity Action and Perception In The Pa-loopmentioning

confidence: 99%

“…These include empowerment Klyubin et al (2005), autonomy (Bertschinger et al, 2008), decisions (Tishby and Polani, 2011), and embodiment (Zahedi and Ay, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Action and perception for spatiotemporal patterns

Biehl¹,

Polani²

2017

Proceedings of the 14th European Conference on Artificial Life ECAL 2017

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This is a contribution to the formalization of the concept of agents in multivariate Markov chains. Agents are commonly defined as entities that act, perceive, and are goal-directed. In a multivariate Markov chain (e.g. a cellular automaton) the transition matrix completely determines the dynamics. This seems to contradict the possibility of acting entities within such a system. Here we present definitions of actions and perceptions within multivariate Markov chains based on entitysets. Entity-sets represent a largely independent choice of a set of spatiotemporal patterns that are considered as all the entities within the Markov chain. For example, the entityset can be chosen according to operational closure conditions or complete specific integration. Importantly, the perceptionaction loop also induces an entity-set and is a multivariate Markov chain. We then show that our definition of actions leads to non-heteronomy and that of perceptions specialize to the usual concept of perception in the perception-action loop.

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“…An example of the latter approach are the information theoretic individuals of Krakauer et al [16]. These individuals are identified using an information theoretic notion of autonomy due to Bertschinger et al [17]. The latter notion of autonomy is also somewhat related to the idea of integration here.…”

Section: Related Workmentioning

confidence: 99%

Specific and Complete Local Integration of Patterns in Bayesian Networks

Biehl

Ikegami

Polani

2017

Entropy

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Abstract:We present a first formal analysis of specific and complete local integration. Complete local integration was previously proposed as a criterion for detecting entities or wholes in distributed dynamical systems. Such entities in turn were conceived to form the basis of a theory of emergence of agents within dynamical systems. Here, we give a more thorough account of the underlying formal measures. The main contribution is the disintegration theorem which reveals a special role of completely locally integrated patterns (what we call ι-entities) within the trajectories they occur in. Apart from proving this theorem we introduce the disintegration hierarchy and its refinement-free version as a way to structure the patterns in a trajectory. Furthermore, we construct the least upper bound and provide a candidate for the greatest lower bound of specific local integration. Finally, we calculate the ι-entities in small example systems as a first sanity check and find that ι-entities largely fulfil simple expectations.

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Autonomy: An information theoretic perspective

Cited by 81 publications

References 13 publications

Information Decomposition and Synergy

Information Decomposition and Synergy

Action and perception for spatiotemporal patterns

Specific and Complete Local Integration of Patterns in Bayesian Networks

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