Opinion Dynamics Explain Price Formation in Prediction Markets

Valerio Restocchi, Frank McGroarty, Enrico Gerding and Markus Brede
Entropy 2023, 25(8), 1152; DOI: 10.3390/e25081152

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.

Read the full article at: www.mdpi.com

Evolution “On Purpose” Teleonomy in Living Systems. Edited by Peter A. Corning, Stuart A. Kauffman, Denis Noble, James A. Shapiro, Richard I. Vane-Wright and Addy Pross

A unique exploration of teleonomy—also known as “evolved purposiveness”—as a major influence in evolution by a broad range of specialists in biology and the philosophy of science.

The evolved purposiveness of living systems, termed “teleonomy” by chronobiologist Colin Pittendrigh, has been both a major outcome and causal factor in the history of life on Earth. Many theorists have appreciated this over the years, going back to Lamarck and even Darwin in the nineteenth century. In the mid-twentieth century, however, the complex, dynamic process of evolution was simplified into the one-way, bottom-up, single gene-centered paradigm widely known as the modern synthesis. In Evolution “On Purpose,” edited by Peter A. Corning, Stuart A. Kauffman, Denis Noble, James A. Shapiro, Richard I. Vane-Wright, and Addy Pross, some twenty theorists attempt to modify this reductive approach by exploring in depth the different ways in which living systems have themselves shaped the course of evolution.

Evolution “On Purpose” puts forward a more inclusive theoretical synthesis that goes far beyond the underlying principles and assumptions of the modern synthesis to accommodate work since the 1950s in molecular genetics, developmental biology, epigenetic inheritance, genomics, multilevel selection, niche construction, physiology, behavior, biosemiotics, chemical reaction theory, and other fields. In the view of the authors, active biological processes are responsible for the direction and the rate of evolution. Essays in this collection grapple with topics from the two-way “read-write” genome to cognition and decision-making in plants to the niche-construction activities of many organisms to the self-making evolution of humankind. As this collection compellingly shows, and as bacterial geneticist James Shapiro emphasizes, “The capacity of living organisms to alter their own heredity is undeniable.”

More at: mitpress.mit.edu

The “Adjacent Possible” – and How It Explains Human Innovation | Stuart Kauffman | TED


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From the astonishing evolutionary advances of the Cambrian explosion to our present-day computing revolution, the trend of dramatic growth after periods of stability can be explained through the theory of the “adjacent possible,” says theoretical biologist Stuart Kauffman. Tracing the arc of human history through the tools and technologies we’ve invented, he explains the impact human ingenuity has had on the planet — and calls for a shift towards more protection for all life on Earth.

Watch at: www.youtube.com

Biology in AI: New Frontiers in Hardware, Software, and Wetware Modeling of Cognition

Luisa Damiano, Pasquale Stano

Artificial Life (2023) 29 (3): 289–292.

The proposal for this special issue was inspired by the main themes around which we organize a series of satellite workshops at Artificial Life conferences (including some of the latest European Conferences on Artificial Life), the title of which is “SB-AI: What can Synthetic Biology (SB) offer to Artificial Intelligence (AI)?” The workshop themes are part of a larger scenario in which we are interested and which we intend to develop. This scenario includes the entire taxonomy of new research frontiers generated within AI, based on the construction and experimental exploration of software, hardware, wetware, and mixed synthetic models to deepen the scientific understanding of biological cognition.

Read the full article at: direct.mit.edu

Artificial intelligence is ineffective and potentially harmful for fact checking

Matthew R. DeVerna, Harry Yaojun Yan, Kai-Cheng Yang, Filippo Menczer

Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent artificial intelligence (AI) language models have shown impressive ability in fact-checking tasks, but how humans interact with fact-checking information provided by these models is unclear. Here we investigate the impact of fact checks generated by a popular AI model on belief in, and sharing intent of, political news in a preregistered randomized control experiment. Although the AI performs reasonably well in debunking false headlines, we find that it does not significantly affect participants’ ability to discern headline accuracy or share accurate news. However, the AI fact-checker is harmful in specific cases: it decreases beliefs in true headlines that it mislabels as false and increases beliefs for false headlines that it is unsure about. On the positive side, the AI increases sharing intents for correctly labeled true headlines. When participants are given the option to view AI fact checks and choose to do so, they are significantly more likely to share both true and false news but only more likely to believe false news. Our findings highlight an important source of potential harm stemming from AI applications and underscore the critical need for policies to prevent or mitigate such unintended consequences.

Read the full article at: arxiv.org