Complexity Theory, Adaptation, and Administrative Law

Recently, commentators have applied insights from complexity theory to legal analysis generally and to administrative law in particular. This Article focuses on one of the central problems that complexity. theory addresses, the importance and mechanisms of adaptation within complex systems. In Part I, the Article uses three features of complex adaptive systems-emergence from self-assembly, nonlinearity, and sensitivity to initial conditions-and explores the extent to which they may add value as a matter of positive analysis to the understanding of change within legal systems. In Part H, the Article focuses on three normative claims in public law scholarship that depend explicitly or implicitly on notions of adaptation: that states offer advantages over the federal government because experimentation can make them more adaptive, that federal agencies should themselves become more experimentalist using the tool of adaptive management, and that administrative agencies shou Id adopt collaborative mechanisms in policymaking. Using two analytic tools found in the complexity literature, the genetic algorithm and evolutionary game theory, the Article tests the extent to which these three normative claims are borne out

On the origin of ambiguity in efficient communication

This article studies the emergence of ambiguity in communication through the concept of logical irreversibility and within the framework of Shannon's information theory. This leads us to a precise and general expression of the intuition behind Zipf's vocabulary balance in terms of a symmetry equation between the complexities of the coding and the decoding processes that imposes an unavoidable amount of logical uncertainty in natural communication. Accordingly, the emergence of irreversible computations is required if the complexities of the coding and the decoding processes are balanced in a symmetric scenario, which means that the emergence of ambiguous codes is a necessary condition for natural communication to succeed.Comment: 28 pages, 2 figure

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Simplistic vs. Complex Organization: Markets, Hierarchies, and Networks in an 'Organizational Triangle'

Transaction cost economics explains organizations in a simplistic ‘market-vs.-hierarchy’ dichotomy. In this view, complex real-world coordination forms are simply considered ‘hybrids’ of those ‘pure’ and ideal forms, thus being located on a one-dimensional ‘line’ between them. This ‘organizational dichotomy’ is mainly based on relative marginal transaction costs, relative lengths of value-added chains, and ‘rational choice’ of coordination form. The present paper, in contrast, argues that pure ‘market’ and ‘hierarchy’, even including their potential hybrids, are a theoretically untenable and empirically void set. Coordination forms, it is argued, have to be conceptualized in a fundamentally different way. A relevant ‘organizational space’ must reflect the dimensions of a complex world such as dilemma-prone direct interdependence, resulting in strong strategic uncertainty, mutual externalities, collectivities, and subsequent emergent process. This, in turn, will lead either to (1) informally institutionalized, problem-solving cooperation (the instrumental dimension of the institution) or (2) mutual blockage, lock-in on an inferior path, or power- and status-based market and hierarchy failure (the ceremonial dimension of the institution). This paper establishes emergent instrumental institutionalized cooperation as a genuine organizational dimension which generates a third ‘attractor’ besides ‘market’ and ‘hierarchy’, i.e., informal network. In this way, an ‘organizational triangle’ can be generated which may serve as a more relevant heuristic device for empirical organizational research. Its ideal corners and some ideal hybrids on its edges (such as ideal clusters and ideal hub&spoke networks) still remain empirically void, but its inner space becomes empirically relevant and accessible. The ‘Organizational Triangle’ is tentatively applied (besides casual reference to corporate behavior that has lead to the current financial meltdown), by way of a set of criteria for instrumental problem-solving and a simple formal algorithm, to the cases of the supplier network of ‘DaimlerChrysler US International’ at Tuscaloosa, AL, the open-source network Linux, and the web-platforms Wikipedia and ‘Open-Source Car’. It is considered to properly reflect what is generally theorized in evolutionary-institutional economics of organizations and the firm and might offer some insight for the coming industrial reconstructions of the car and other industries.Market vs. Hierarchy; Transaction Costs; Complexity; Institutionalization; Network Formation; Hub&Spoke Supplier Networks; Open-Source Networks

How to Solve AI Bias

© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Bias in AI is a topic that impacts machine learning and artificial intelligence technology that learns from datasets and its training data. While gender discrimination and chatbots showing bias have recently caught people’s attention and imagination, the overall area of how to correct and manage bias is in its infancy for business use. Further, little is known about how to solve bias in AI and how there could potent for malicious misuse at large scale. We explore this area and propose solutions to this problem.Non peer reviewe

Adaptive Investment Strategies For Periodic Environments

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure