Integrated Information Increases with Fitness in the Evolution of Animats

Abstract

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent (“animat”) evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its “fit” to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.

The Intrinsic Cause-Effect Power of Discrete Dynamical Systems—From Elementary Cellular Automata to Adapting Animats

“…In addition to the state entropy, in [19] we also assessed how the sensory-motor mutual information (ISMMI) [34] and predictive information (IPred) [35] of the animats as defined in [19,36] evolved during adaptation. ISMMI measures the differentiation of the observed input-output behavior of the animats' sensors and motors.…”

Evolution of sustained foraging in three-dimensional environments with physics

“…It would also be instructive to use exactly the same tools and the same parameters to evolve foragers with different types of controllers, such as continuous time recurrent neural networks, as in Refs. [37,81,82], or radically different ones such as the Markov networks in [83,84] and see what previous evolutionary obstacles they ease, or which new ones they impose.…”

Information Geometry on Complexity and Stochastic Interaction

“…I shall focus on approaches that aim at relating such information based principles to the overall complexity of the system. In particular, I shall concentrate on the theory of information integration and complexity, initially proposed by Tononi, Sporns, and Edelman [9], and further developed and analyzed in a series of papers [10][11][12][13][14][15]. I shall compare this line of research with my own information-geometric approach to complexity, initially proposed in my manuscript [16], entitled Information Geometry on Complexity and Stochastic Interaction, which led to various lines of research that I am going to outline below.…”

“…However, by the end of the unusually long reviewing process I had come to the conclusion that my geometric approach has to be further improved in order to address important aspects of complexity (I shall be more concrete on that). Recent developments, on the other hand, suggest that this work is of relevance in the context of information integration already in its present form [12][13][14][15]20,21]. Therefore, it should be useful to provide it together with a discussion of its strengths and shortcomings, thereby relating it to similar work that has been developed since its first publication.…”