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Pedro A. M. Mediano
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Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (3): 182–198.
Published: 01 November 2018
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The idea that an agent's actions can impact its actual long-term survival is a very appealing one, underlying influential treatments such as Di Paolo's (2005). However, this presents a tension with understanding the agent and environment as possessing specific objective physical microstates. More specifically, we show that such an approach leads to undesirable outcomes, for example, all organisms being maladaptive on average. We suggest that this problematic intuition of improvement over time may stem from Bayesian inference. We illustrate our arguments using a recent model of autopoietic agency in a model protocell, showing the limitations of previous approaches in this model and specific instantiations of Bayesian inference by ignorant observers in certain scenarios.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2018) 24 (3): 199–217.
Published: 01 November 2018
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One important sense of the term “adaptation” is the process by which an agent changes appropriately in response to new information provided by environmental stimuli. We propose a novel quantitative measure of this phenomenon, which extends a little-known definition of adaptation as “increased robustness to repeated perturbation” proposed by Klyubin (2002). Our proposed definition essentially corresponds to the average value (relative to some fitness function) of state changes that are caused by the environment (in some statistical ensemble of environments). We compute this value by comparing the agent's actual fitness with its fitness in a counterfactual world where the causal links between agent and environment are disrupted. The proposed measure is illustrated in a simple Markov chain model and also using a recent model of autopoietic agency in a simulated protocell.