Naturalizing institutions: Evolutionary principles and application on the case of money

In recent extensions of the Darwinian paradigm into economics, the replicator-interactor duality looms large. I propose a strictly naturalistic approach to this duality in the context of the theory of institutions, which means that its use is seen as being always and necessarily dependent on identifying a physical realization. I introduce a general framework for the analysis of institutions, which synthesizes Searle's and Aoki's theories, especially with regard to the role of public representations (signs) in the coordination of actions, and the function of cognitive processes that underly rule-following as a behavioral disposition. This allows to conceive institutions as causal circuits that connect the population-level dynamics of interactions with cognitive phenomena on the individual level. Those cognitive phenomena ultimately root in neuronal structures. So, I draw on a critical restatement of the concept of the meme by Aunger to propose a new conceptualization of the replicator in the context of institutions, namely, the replicator is a causal conjunction between signs and neuronal structures which undergirds the dispositions that generate rule-following actions. Signs, in turn, are outcomes of population-level interactions. I apply this framework on the case of money, analyzing the emotions that go along with the use of money, and presenting a stylized account of the emergence of money in terms of the naturalized Searle-Aoki model. In this view, money is a neuronally anchored metaphor for emotions relating with social exchange and reciprocity. Money as a meme is physically realized in a replicator which is a causal conjunction of money artefacts and money emotions. --Generalized Darwinism,institutions,replicator/interactor,Searle,Aoki,naturalism,memes,emotions,money

Towards Believable Resource Gathering Behaviours in Real-time Strategy Games with a Memetic Ant Colony System

AbstractIn this paper, the resource gathering problem in real-time strategy (RTS) games, is modeled as a path-finding problem where game agents responsible for gathering resources, also known as harvesters, are only equipped with the knowledge of its immediate sur- roundings and must gather knowledge about the dynamics of the navigation graph that it resides on by sharing information and cooperating with other agents in the game environment. This paper proposed the conceptual modeling of a memetic ant colony system (MACS) for believable resource gathering in RTS games. In the proposed MACS, the harvester's path-finding and resource gathering knowledge captured are extracted and represented as memes, which are internally encoded as state transition rules (mem- otype), and externally expressed as ant pheromone on the graph edge (sociotype). Through the inter-play between the memetic evolution and ant colony, harvesters as memetic automatons spawned from an ant colony are able to acquire increasing level of capability in exploring complex dynamic game environment and gathering resources in an adaptive manner, producing consistent and impressive resource gathering behaviors

Human-level Atari 200x faster

The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subject of research of a large body of work, with performance frequently measured by observing scores over the wide range of environments contained in the Atari 57 benchmark. Agent57 was the first agent to surpass the human benchmark on all 57 games, but this came at the cost of poor data-efficiency, requiring nearly 80 billion frames of experience to achieve. Taking Agent57 as a starting point, we employ a diverse set of strategies to achieve a 200-fold reduction of experience needed to out perform the human baseline. We investigate a range of instabilities and bottlenecks we encountered while reducing the data regime, and propose effective solutions to build a more robust and efficient agent. We also demonstrate competitive performance with high-performing methods such as Muesli and MuZero. The four key components to our approach are (1) an approximate trust region method which enables stable bootstrapping from the online network, (2) a normalisation scheme for the loss and priorities which improves robustness when learning a set of value functions with a wide range of scales, (3) an improved architecture employing techniques from NFNets in order to leverage deeper networks without the need for normalization layers, and (4) a policy distillation method which serves to smooth out the instantaneous greedy policy overtime

Unlocking the Power of Representations in Long-term Novelty-based Exploration

We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of Deep RL, RECODE can efficiently track state visitation counts over thousands of episodes. We further propose a novel generalization of the inverse dynamics loss, which leverages masked transformer architectures for multi-step prediction; which in conjunction with RECODE achieves a new state-of-the-art in a suite of challenging 3D-exploration tasks in DM-Hard-8. RECODE also sets new state-of-the-art in hard exploration Atari games, and is the first agent to reach the end screen in "Pitfall!"

A particle swarm optimization-based algorithm for finding gapped motifs

<p>Abstract</p> <p>Background</p> <p>Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions.</p> <p>Results</p> <p>In this work, we develop a novel motif finding algorithm (PSO+) using a population-based stochastic optimization technique called Particle Swarm Optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the latter. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method allows the presence of input sequences containing zero or multiple binding sites.</p> <p>Conclusion</p> <p>Experimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.</p

Spartan Daily, March 8, 2017

Volume 148, Issue 18https://scholarworks.sjsu.edu/spartan_daily_2017/1017/thumbnail.jp

The naturalistic turn in economics: implications for the theory of finance

Economics is increasingly adopting the methodological standards and procedures of the natural sciences. The paper analyzes this 'naturalistic turn' from the philosophical perspective on naturalism, and I discuss the implications for the field of finance. The theory of finance is an interesting case in point for the methodological issues, as it manifests a paradigmatic tension between the pure theory of finance and Behavioral Finance. I distinguish between three kinds of naturalism: mark I, the reduction of behavior on psychoneural phenomena, mark II, the transfer of patterns of causal explanations from the natural sciences to the social sciences, mark III, the enrichment of the ontology from observer-independent to observer-relative facts. Building an integrated naturalistic paradigm from these three ingredients, I show that naturalism in economics will only be completed by a simultaneous linguistic turn, with language being analyzed from the naturalistic viewpoint. I relate this proposition with recent results of research into finance, especially connecting Behavioral Finance with the sociology of finance. --Naturalism,causation in economics,neuroeconomics,behavioral finance,social ontology,sociology of finance

Neuroeconomics, naturalism and language

Neuroeconomics stays in the center of the ongoing naturalistic turn in economics. It portrays the individual as a complex system of decision making mechanisms and modules. This results into a conceptual tension with the standard economic notion of the unity of the actor that is a systemic property of economic coordination. I propose to supplement neuroeconomics with a naturalistic theory of social coordination. Recent neurobiological and psychological research strongly supports claims made by some heterodox economists that the identity of actors emerges from social interaction, especially in the context of the use of language. Therefore, I argue that the completion of the neuroeconomic paradigm requires a naturalistic theory of language. I provide some sketches based on teleosemantics and memetics, and exemplify the argument by a naturalist account of money. --Naturalism,neuroeconomics,individual identity,language and economics,naturalistic theory of social interaction