Gradient steps to an ecology of mind

Regularised survival of the fittest

November 27, 2011 — March 20, 2024

collective knowledge
extended self
game theory
incentive mechanisms
social graph
Figure 1

At social brain I wonder how we (humans) behave socially, and evolutionarily.

Here I wonder if consciousness is intrinsically social, and whether non social intelligences are a problem for consciousness. What ethics will execute on their moral wetware?

…is consciousness that good anyway?

1 Loss functions versus survival functions


Figure 2

2 Incoming

Neural Annealing: Toward a Neural Theory of Everything.

Predictive Coding has been Unified with Backpropagation, concerning Millidge, Tschantz, and Buckley (2020). I have not read the article or the explanation properly, but at first glance it indicates that perhaps I do not understand this area properly. The assertion, skim-read, seems to be that predictive coding, which I imagined was some form of variational inference, can approximate minimum loss learning by backpropagation in some sense. While not precisely trivial, this would seem like well-trodden ground— unless I have failed to understand how they are using the terms, which seems likely. TBC.

  • Gradient Dissent, a list of reasons that large backpropagation-trained networks might be worrisome. There are some interesting points in there, and some hyperbole. Also: If it were true that there are externalities from backprop networks (i.e. that they are a kind of methodological pollution that produces private benefits but public costs) then what kind of mechanisms should be applied to disincentivise them?
  • C&C Against Predictive Optimization
Figure 3

Professor Javen Qinfeng Shi says:

Mind is a choice maker. Choices shape the mind

  • Q learning: do what a good/kind person would do (moment to moment), learn wisdom (V function) and have faith in future and self-growth. It naturally leads to optimal long term accumulative rewards (Bellman equation)
  • Policy gradient: learn from past successes (to repeat or mimic) and mistakes (to avoid). Require complete episodes to reveal the end accumulative reward per episode

This is the first time I have heard of policy gradient as utilitarianism versus Q learning as virtue ethics.

3 References

Acemoglu, and Ozdaglar. 2011. Opinion Dynamics and Learning in Social Networks.” Dynamic Games and Applications.
Aktipis. 2016. Principles of Cooperation Across Systems: From Human Sharing to Multicellularity and Cancer.” Evolutionary Applications.
Altaner. 2017. Nonequilibrium Thermodynamics and Information Theory: Basic Concepts and Relaxing Dynamics.” Journal of Physics A: Mathematical and Theoretical.
Axelrod, Robert M. 1984. The evolution of cooperation.
Axelrod, Robert, and Hamilton. 1981. The Evolution of Cooperation.” Science, New Series,.
Beaulieu, Frati, Miconi, et al. 2020. Learning to Continually Learn.”
Beretta. 2020. The Fourth Law of Thermodynamics: Steepest Entropy Ascent.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
Bieniawski, and Wolpert. 2004. Adaptive, Distributed Control of Constrained Multi-Agent Systems.” In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3.
Bowles, Choi, and Hopfensitz. 2003. The Co-Evolution of Individual Behaviors and Social Institutions.” Journal of Theoretical Biology.
Boyd, A. B., Crutchfield, and Gu. 2020. Thermodynamic Machine Learning Through Maximum Work Production.”
Boyd, Robert, and Richerson. 1999. “Complex Societies: The Evolutionary Origins of a Crude Superorganism.” Human Nature.
Fletcher, and Zwick. 2007. The evolution of altruism: game theory in multilevel selection and inclusive fitness.” Journal of Theoretical Biology.
Galesic, Barkoczi, Berdahl, et al. 2022. Beyond Collective Intelligence: Collective Adaptation.”
Gintis, Bowles, Boyd, et al. 2003. Explaining Altruistic Behavior in Humans.” Evolution and Human Behavior.
Gopnik. 2020. Childhood as a Solution to Explore–Exploit Tensions.” Philosophical Transactions of the Royal Society B: Biological Sciences.
Hagens. 2020. Economics for the Future – Beyond the Superorganism.” Ecological Economics.
Harper. 2009. The Replicator Equation as an Inference Dynamic.”
Hasegawa, and Van Vu. 2019. Uncertainty Relations in Stochastic Processes: An Information Inequality Approach.” Physical Review E.
Ha, and Tang. 2022. Collective Intelligence for Deep Learning: A Survey of Recent Developments.” Collective Intelligence.
Hazlett. 2013. A Luxury of the Understanding: On the Value of True Belief.
Henrich, and Boyd. 1998. The Evolution of Conformist Transmission and the Emergence of Between-Group Differences.” Evolution and Human Behavior.
Henrich, and Gil-White. 2001. The Evolution of Prestige: Freely Conferred Deference as a Mechanism for Enhancing the Benefits of Cultural Transmission.” Evolution and Human Behavior.
Hertz, Romand-Monnier, Kyriakopoulou, et al. 2016. Social influence protects collective decision making from equality bias.” Journal of Experimental Psychology. Human Perception and Performance.
Hetzer, and Sornette. 2009. Other-Regarding Preferences and Altruistic Punishment: A Darwinian Perspective.” SSRN Scholarly Paper ID 1468517.
Heydari Fard. 2018. Decision-Theoretic Consequentialism and the Desire-Luck Problem.” Journal of Cognition and Neuroethics.
Hoelzemann, and Klein. 2021. Bandits in the Lab.” Quantitative Economics.
Hoffman, and Prakash. 2014. Objects of consciousness.” Frontiers in Psychology.
Hoffman, Singh, and Prakash. 2015. The Interface Theory of Perception.” Psychonomic Bulletin & Review.
Hunt. n.d. The ‘Easy Part’ of the Hard Problem: A Resonance Theory of Consciousness.”
Judson. 2017. The Energy Expansions of Evolution.” Nature Ecology & Evolution.
Kolchinsky, and Wolpert. 2018. Semantic Information, Autonomous Agency, and Nonequilibrium Statistical Physics.”
Krakauer, Page, and Erwin. 2009. Diversity, Dilemmas, and Monopolies of Niche Construction. The American Naturalist.
Krzakala, Zdeborova, Angelini, et al. n.d. Statistical Physics of Inference and Bayesian Estimation.”
Lang, Fisher, Mora, et al. 2014. Thermodynamics of Statistical Inference by Cells.” Physical Review Letters.
Lehman, and Stanley. 2011. Abandoning Objectives: Evolution Through the Search for Novelty Alone.” Evolutionary Computation.
———. 2013. Evolvability Is Inevitable: Increasing Evolvability Without the Pressure to Adapt.” PLoS ONE.
Mandelbrot. 1962. The Role of Sufficiency and of Estimation in Thermodynamics.” The Annals of Mathematical Statistics.
Mark, Marion, and Hoffman. 2010. Natural Selection and Veridical Perceptions.” Journal of Theoretical Biology.
McElreath, and Boyd. 2007. Mathematical Models of Social Evolution: A Guide for the Perplexed.
Mercier, and Sperber. 2011a. Argumentation: Its Adaptiveness and Efficacy.” Behavioral and Brain Sciences.
———. 2011b. Why Do Humans Reason? Arguments for an Argumentative Theory.” Behavioral and Brain Sciences.
———. 2017. The Enigma of Reason.
Millidge, Tschantz, and Buckley. 2020. Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs.” arXiv:2006.04182 [Cs].
Moral Sentiments and Material Interests: The Foundations of Cooperation in Economic Life. 2006.
Morowitz, and Smith. 2007. Energy Flow and the Organization of Life.” Complexity.
Nowak. 2006. Five Rules for the Evolution of Cooperation.” Science.
O’Connor. 2017. Evolving to Generalize: Trading Precision for Speed.” British Journal for the Philosophy of Science.
Odum. 1973. Energy, Ecology, and Economics.” Ambio.
Omohundro. 2008. The Basic AI Drives.” In Proceedings of the 2008 Conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference.
Perunov, Marsland, and England. 2016. Statistical Physics of Adaptation.” Physical Review X.
Poole, Lahiri, Raghu, et al. 2016. Exponential Expressivity in Deep Neural Networks Through Transient Chaos.” In Advances in Neural Information Processing Systems 29.
Prakash, Fields, Hoffman, et al. 2020. Fact, Fiction, and Fitness.” Entropy.
Prakash, Stephens, Hoffman, et al. 2021. Fitness Beats Truth in the Evolution of Perception.” Acta Biotheoretica.
Ricotta, and Szeidl. 2006. Towards a Unifying Approach to Diversity Measures: Bridging the Gap Between the Shannon Entropy and Rao’s Quadratic Index.” Theoretical Population Biology.
Ringstrom. 2022. Reward Is Not Necessary: How to Create a Compositional Self-Preserving Agent for Life-Long Learning.”
Schneider, and Kay. 1994. “Life as a Manifestation of the Second Law of Thermodynamics.” Mathematical and Computer Modelling.
Shwartz-Ziv, and Tishby. 2017. Opening the Black Box of Deep Neural Networks via Information.” arXiv:1703.00810 [Cs].
Smith. 2008. Thermodynamics of Natural Selection III: Landauer’s Principle in Computation and Chemistry.” Journal of Theoretical Biology.
Sperber, and Mercier. 2012. Reasoning as a Social Competence.” In Collective Wisdom.
Stiglitz. 2006. The Contributions of the Economics of Information to Twentieth Century Economics.” The Quarterly Journal of Economics.
Still, Sivak, Bell, et al. 2012. Thermodynamics of Prediction.” Physical Review Letters.
Székely, and Rizzo. 2017. The Energy of Data.” Annual Review of Statistics and Its Application.
Thagard. 1997. “Collaborative Knowledge.” Noûs.
Ulanowicz, and Abarca-Arenas. 1997. An Informational Synthesis of Ecosystem Structure and Function.” Ecological Modelling.
Ulanowicz, Goerner, Lietaer, et al. 2009. Quantifying Sustainability: Resilience, Efficiency and the Return of Information Theory.” Ecological Complexity.
Watson, Levin, and Buckley. 2022. Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality.” Frontiers in Ecology and Evolution.
Weng, Flammini, Vespignani, et al. 2012. Competition Among Memes in a World with Limited Attention.” Scientific Reports.
Wolpert, David H. 2006a. “Advances in Distributed Optimization Using Probability Collectives.” Advances in Complex Systems.
———. 2006b. Information Theory — The Bridge Connecting Bounded Rational Game Theory and Statistical Physics.” In Complex Engineered Systems. Understanding Complex Systems.
Wolpert, David H. 2008. Physical Limits of Inference.” Physica D: Nonlinear Phenomena, Novel Computing Paradigms: Quo Vadis?,.
———. 2016. The Free Energy Requirements of Biological Organisms; Implications for Evolution.”
Wolpert, David. 2017. Constraints on Physical Reality Arising from a Formalization of Knowledge.”
Wolpert, David H. 2018. Theories of Knowledge and Theories of Everything.” In The Map and the Territory: Exploring the Foundations of Science, Thought and Reality.
———. 2019. Stochastic Thermodynamics of Computation.”
———. 2021. Fluctuation Theorems for Multiple Co-Evolving Systems.” arXiv:2003.11144 [Cond-Mat].
Wolpert, David H, Bieniawski, and Rajnarayan. 2011. “Probability Collectives in Optimization.”
Wolpert, David H., and Tumer. 1999. An Introduction to Collective Intelligence.” arXiv:cs/9908014.
Wolpert, David H, Wheeler, and Tumer. 1999. General Principles of Learning-Based Multi-Agent Systems.” In.
Zdeborová, and Krzakala. 2016. Statistical Physics of Inference: Thresholds and Algorithms.” Advances in Physics.
Zhuang, and Hadfield-Menell. 2021. Consequences of Misaligned AI.”