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Simon McGregor
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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.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2016) 22 (3): 408–423.
Published: 01 August 2016
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We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1 .
Journal Articles
Publisher: Journals Gateway
Artificial Life (2016) 22 (2): 138–152.
Published: 01 May 2016
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Life on Earth must originally have arisen from abiotic chemistry. Since the details of this chemistry are unknown, we wish to understand, in general, which types of chemistry can lead to complex, lifelike behavior. Here we show that even very simple chemistries in the thermodynamically reversible regime can self-organize to form complex autocatalytic cycles, with the catalytic effects emerging from the network structure. We demonstrate this with a very simple but thermodynamically reasonable artificial chemistry model. By suppressing the direct reaction from reactants to products, we obtain the simplest kind of autocatalytic cycle, resulting in exponential growth. When these simple first-order cycles are prevented from forming, the system achieves superexponential growth through more complex, higher-order autocatalytic cycles. This leads to nonlinear phenomena such as oscillations and bistability, the latter of which is of particular interest regarding the origins of life.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2005) 11 (4): 459–472.
Published: 01 October 2005
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We present a novel formal interpretation of dynamical hierarchies based on information theory, in which each level is a near-state-determined system, and levels are related to one another in a partial ordering. This reformulation moves away from previous definitions, which have considered unique hierarchies of structures or objects arranged in aggregates. Instead, we consider hierarchies of dynamical systems: these are more suited to describing living systems, which are not mere aggregates, but organizations. Transformations from lower to higher levels in a hierarchy are redescriptions that lose information. There are two criteria for partial ordering. One is a state-dependence criterion enforcing predictability within a level. The second is a distinctness criterion enforcing the idea that the higher-level description must do more than just throw information away. We hope this will be a useful tool for empirical studies of both computational and physical dynamical hierarchies.