The backward induction controversy as a metaphorical problem

The backward induction controversy in game theory flared up and then practically ended within a decade - the 1990s. The protagonists, however, did not converge on an agreement about the source of the controversy. Why was this the case, if opposing sides had access to the same modelling techniques and empirical facts? In this paper I offer an explanation for this controversy and its unsettled end. The answer is not to be found in the modelling claims made by the opposing protagonists, but in the tacit metaphors they operate under. Aristotle defined metaphor as giving a 'thing a name that belongs to something else' (Poetica, 1457b). The meaning of metaphors has not changed much since then - in contrast to models which are comparatively new, and still not well-understood, scientific tools. The controversy of backward induction in game theory provides a test bed for the explanatory power of metaphors. This paper frames the controversy in terms of metaphor choice to provide a common framework for the protagonists. This results in the identification of three different domains - mathematical logic, game theory and the world - each connected to the other via different metaphors. The controversy around backward induction is placed in, and tentatively explained by, this framework. © 2018, World Economics Association

John Stuart Mill, soft paternalist

According to John Stuart Mill’s (1806–1873) Liberty Principle, when certain social and cognitive conditions are satisfied and as long as no one else is harmed, an individual’s self-regarding thoughts and actions ought to be protected from interference. The Liberty Principle forged the identity of Mill as a liberal and an anti-paternalist. Almost two centuries later, in fact, Mill is a figurehead for attacks by the new paternalists emerging from the behavioral sciences, in particular behavioral economics. The alleged discoveries of predictable errors in decision-making, and the ensuing corrective soft paternalistic policies, appear to clash with his Liberty Principle in so far as they allow interference with self-regrading acts even when no one else is harmed. This paper questions this narrative and posits that Mill saw favorably choice preserving interventions even when a self-regarding act harmed no one but the individual; he did not object to interference with liberty if individuals are deemed mentally incapacitated, if their self-regarding acts harmed others, or if their acts lead to abnegation of their own freedom. Finally, Mill’s Liberty Principle generates tensions with his doctrine of Free Trade and may not be employed without further qualifications in defense of free markets. I conclude by encouraging the soft paternalists to integrate Mill’s original thoughts on liberty in their work since, like them, he sought the best mix of policies that promote freedom and welfare. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

Population Descent: A Natural-Selection Based Hyper-Parameter Tuning Framework

First-order gradient descent has been the base of the most successful optimization algorithms ever implemented. On supervised learning problems with very high dimensionality, such as neural network optimization, it is almost always the algorithm of choice, mainly due to its memory and computational efficiency. However, it is a classical result in optimization that gradient descent converges to local minima on non-convex functions. Even more importantly, in certain high-dimensional cases, escaping the plateaus of large saddle points becomes intractable. On the other hand, black-box optimization methods are not sensitive to the local structure of a loss function's landscape but suffer the curse of dimensionality. Instead, memetic algorithms aim to combine the benefits of both. Inspired by this, we present Population Descent, a memetic algorithm focused on hyperparameter optimization. We show that an adaptive m-elitist selection approach combined with a normalized-fitness-based randomization scheme outperforms more complex state-of-the-art algorithms by up to 13% on common benchmark tasks

PULER

We address the prevalent challenge of Algebraic Data Type duplication in compiler implementations, which results in increased effort, diminished functionality, and complications in synchronizing language constructs across the compiler. To investigate a novel design solution, we present Tree Shaping, a solution to the expression problem. We then implement an experimental compiler using Tree Shaping and examine its potential implications. This compiler processes programs written in PULER, an ML-based programming language that boasts distinct features such as unification rules for type mismatches. Contrary to traditional compilers that terminate and generate an error when encountering a type mismatch, PULER regards type mismatches as first-class citizens

Catalysis of N-Nitrosamine formation by bacteria

Thesis (Ph. D.)--Michigan State University. Department of Food Science and Human Nutrition, 1981Includes bibliographical references (pages 108-124

Activation of Bacillus stearothermophilus spores

Thesis (M.S.)--Michigan State University. Dept. of Food Science and Human Nutrition,Includes bibliographical references (leaves 38-43