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Keith L. Downing
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Journal Articles
Publisher: Journals Gateway
Artificial Life (2023) 29 (4): 394–420.
Published: 01 November 2023
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The prevalence of artificial intelligence (AI) tools that filter the information given to internet users, such as recommender systems and diverse personalizers , may be creating troubling long-term side effects to the obvious short-term conveniences. Many worry that these automated influencers can subtly and unwittingly nudge individuals toward conformity, thereby (somewhat paradoxically) restricting the choices of each agent and/or the population as a whole. In its various guises, this problem has labels such as filter bubble , echo chamber , and personalization polarization . One key danger of diversity reduction is that it plays into the hands of a cadre of self-interested online actors who can leverage conformity to more easily predict and then control users’ sentiments and behaviors, often in the direction of increased conformity and even greater ease of control. This emerging positive feedback loop and the compliance that fuels it are the focal points of this article, which presents several simple, abstract, agent-based models of both peer-to-peer and AI-to-user influence. One of these AI systems functions as a collaborative filter, whereas the other represents an actor the influential power of which derives directly from its ability to predict user behavior. Many versions of the model, with assorted parameter settings, display emergent polarization or universal convergence, but collaborative filtering exerts a weaker homogenizing force than expected. In addition, the combination of basic agents and a self-interested AI predictor yields an emergent positive feedback that can drive the agent population to complete conformity.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2004) 10 (1): 39–63.
Published: 01 January 2004
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Baldwin's classic hypothesis states that behavioral plasticity can speed evolution by (a) smoothing the fitness landscape and (b) indirect genetic assimilation of acquired characteristics. This latter phase demands a strong correlation between genotype and phenotype space. But the natural world shows signs of this correlation at only a very coarse level, since the intervening developmental process greatly complicates the mapping from genetics to physiology and ethology. Hence, development appears to preclude a strong Baldwin effect. However, by adding a simple developmental mechanism to Hinton and Nowlan's classic model of the Baldwin effect, and by allowing evolution to determine the proper balance between direct and indirect mapping of genome to phenotype, this research reveals several different effects of development on the Baldwin effect, some promoting and others inhibiting. Perhaps the most interesting result is an evolved cooperation between direct blueprints and indirect developmental recipes in searching for unstructured and partially structured target patterns in large, needle-in-the-haystack fitness landscapes.
Journal Articles
Publisher: Journals Gateway
Artificial Life (2002) 8 (2): 123–153.
Published: 01 April 2002
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This work continues investigation into Gaia theory [Lovelock, (1995) The ages of Gaia , Oxford University Press] from an artificial life perspective [Downing, (2000) in Proceedings of the 7th International Conference on Artificial Life , (pp. 90–99) MIT Press], with the aim of assessing the general compatibility of emergent distributed environmental control with conventional natural selection. Our earlier system, GUILD [Downing and Zvirinsky, (1999) Artificial Life, 5 , 291–318], displayed emergent regulation of the chemical environment by a population of metabolizing agents, but the chemical model underlying those results was trivial, essentially admitting all possible reactions at a single energy cost. The new model, METAMIC, utilizes abstract chemistries that are both (a) constrained to a small set of legal reactions, and (b) grounded in basic fundamental relationships between energy, entropy, and biomass synthesis/breakdown. To explore the general phenomena of emergent homeostasis, we generate 100 different chemistries and use each as the basis for several METAMIC runs, as part of a Gaia hunt . This search discovers 20 chemistries that support microbial populations capable of regulating a physical environmental factor within their growth-optimal range, despite the extra metabolic cost. Case studies from the Gaia hunt illustrate a few simple mechanisms by which real biota might exploit the underlying chemistry to achieve some control over their physical environment. Although these results shed little light on the question of Gaia on Earth, they support the possibility of emergent environmental control at the microcosmic level.