Bounded Rationality, Heuristics, Computational Complexity, and Artificial Intelligence

The theory of computational complexity is based on the tradeoff between two computational resources, memory space and computer time. This paper investigates the physical counterparts of these resources. Memory space is the number of bits of information required by a computer over the course of a computation. Physically, information is intimately connected with entropy: entropy can be thought of as random bits of information. The physical analogue of the amount of memory space required for a computation, Ф- SPACE, is the difference between the maximum entropy Smax of the computing system and its actual entropy, S.Ф-SPACE = S - Smax is the thermodynamic depth of the computation. Ф-SPACE is the maximum number of bits of memory space available for a physical system to perform computation. In conventional computational complexity theory, computer time is identified as the number of elementary logical operations (AND, OR, NOT, COPY) required to perform the computation. The physical analogue of time computational complexity is simply the time required for the physical computing system to perform the computation. In order to obtain a number that measures the number of physical operations that can be performed by the system over time t, we define Ф-TIME to be proportional to the computation time multiplied by the energy E of the computing system over the course of the computation: Ф-TIME = 2Et/πħ. Here, the proportionality factor 2/πħ is chosen to match the time it takes a computing system with energy E to flip a bit in time t. Ф-TIME is thus the number of bit flips that can take place in an information processing system. In addition, we propose a novel measure of physical complexity that combines these measures. The free energy F = E - TS of a system is the amount of energy that is available to do useful work - as opposed to the energy bound up in the thermal fluctuations of heat. We define the free complexity to be the free energy × time product, 2Ft/πħ required by a physical system to perform a computation. Physical spatial and temporal complexity provide upper bounds on the amount of computation that can be performed by any physical system. We discuss the implications of physical complexity for the computational complexity hierarchy, in particular, we show that in the presence of errors in the computation, Ф-SPACE is proportional Ф-TIME. We apply the resulting theory of physical computational complexity to analyze the computational power of black holes: exactly half of the energy of a black hole is available for physical computation.

To explore the relation between early career performance or recognition and receiving the Nobel Prize in Economic Sciences, we compare winners of the John Bates Clark Medal, the most prestigious early career recognition for economists, with other successful scholars. The initial comparison combines JBCM winners with scholars published in leading economics journals, controlling for educational background (institution conferring the Ph.D.) and publication and citation success. We then narrow the comparison group down to those given relatively early recognition (based on age category) in the form of other major awards. Lastly, we compare the JBCM awardees with synthetic counterfactuals that best resemble their pre-award academic career performance. All three analyses provide strong support for the notion that winning the JBCM is related to receiving the Nobel Prize, the award of which is also correlated with early career performance success as measured by number of publications and citations.

Editorial

Herbert A. Simon, 1916-2001

The Matthew effect has become a standard concept in science studies and beyond to describe processes of cumulative advantage. Despite its wide success, a rigorous quantitative analysis for Merton’s original case for Matthew effects—the Nobel Prize—is still missing. This paper aims to fill this gap by exploring the causal effect of the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (hereafter the Nobel Prize in Economics). Furthermore, we test another of Merton’s ideas: successful papers can draw attention to cited references, leading to a serial diffusion of ideas. Based on the complete Web of Science 1900–2011, we estimate the causal effects of Nobel Prizes compared to a synthetic control group which we constructed by combining different matching techniques. We find clear evidence for a Matthew effect upon citation impacts, especially for papers published within 5 years before the award. Further, scholars from the focal field of the award are particularly receptive to the award signal. In contrast to that, we find no evidence that the Nobel Prize causes a serial diffusion of ideas. Papers cited by future Nobel laureates do not gain in citation impact after the award.

Studying the research results by Nobel prize-winning economists is a fundamentally important task for determining the most promising areas of development of Russian economic science. The author traces the connections between all these works and their predecessors’ work, many of whom were also awarded the Nobel prize in economics. Three lines of development of the predecessors’ achievements are identifid: information asymmetry, limited rationality, the design of market mechanism and new institutionalism. The article does not address the problems of auction theory related to its formal justifiation, which is part of mathematical economics and game theory. However, it is noted that this is an essential part of the laureates’ achievements. The article’s main conclusions are that the prize received for “improving the theory of auctions” fis into the broader economic research context. The winners solved the problems of maintaining free competition and reducing the market’s likelihood of monopolization. The author pays special attention to the results of the empirical analysis of actually implemented auctions. This analysis is divided into three parts, corresponding to the three stages of each auction: starting problems (distrust, the reputation of participants, etc.), the course of the auction (behaviour of participants and results, in particular, the “winner’s curse”.

Urgency of the research of artificial intelligence research in a digital society presents numerous causal changes, among them: space research, biotechnology, nanotechnology, digital technology, etc. The implications for human beings and human civilisation are significant as they affect a number of already existing contemporary issues such as economics, politics, ethics, law, psychology, conflictology, sociology, and ecology. Anthropology has to return to the existence of human beings in a society of artificial intelligence, consciousness and even the physical body. The study conceptualises artificial intelligence as a factor of ecosystem growth in the context of technological, ontological, and anthropological dimensions. The research objectives: 1) to theorise artificial intelligence as a potential and resource for use in all sectors of the socioeconomic sphere in the context of technological dimensions; 2) to clarify the convergence of artificial intelligence and its main components in the context of ontological dimensions; 3) to reveal the role of artificial intelligence in enhancing the digitalisation of society and humans in the context of anthropological dimensions. In the research we are guided by the works of scholars who have studied and are studying the problems of artificial intelligence as a complex social, economic, and cultural phenomenon: R. Andriukaitienė, N. Boström, A. Vance, P. Diamandis, S. Kotler, P. Dickson, L. Kai-Fu, and K. Kell, K. Kell, and others. Research methodology. The theory of artificial intelligence is a complex multidisciplinary science, which is located at the junction of economics, sociology, computer science, mathematics, psychology. Summarizing the processes of interdisciplinary interaction of artificial intelligence, we note that its study requires an interdisciplinary approach based on the synthesis of scientific knowledge, as revealing the content and principles of functioning of a complex invariant system of artificial intelligence. To analyze the modern model of artificial intelligence, it is necessary to apply a systematic institutional-evolutionary approach. By analysing different methodological approaches, it is necessary to acknowledge the presence of an anthropogenic component, as the methodology of scientific research is based on the method of information-anthropogenic analysis based on the fundamental position that no artificial intelligence processes are possible without information/data as an object and human as a subject of cognition. In addition, the following methods were used: induction, deduction, sublimation, synergetics, evolutionary historicism, modelling, and forecasting. The result of the research. Artificial Intelligence (AI) is a broad term used to define technologies/ engineering systems that emulate human intelligence, linking to neurophysiology, robotics, psychology (pattern recognition, modelling of psychological processes), transhumanism, cybernetics (computing power to find patterns in large data sets). Artificial Intelligence has been defined as a software-engineered system that is capable of influencing the environment with varying degrees of autonomy, producing results (predictions, recommendations, decisions) for a specific set of goals for the digitalisation of society. On the other hand, artificial intelligence causes many problems that are seen as cyber/virtual addiction, loss of reality, increased surveillance, loss of protocol control, privacy issues, cybersecurity, etc. The convergence of large data with artificial intelligence has become the single most important development shaping the future of social institutions in the digital society and the prospects for realising potential opportunities. Thanks to the latest digital technologies, advances in software engineering, and evolving artificial intelligence, there are both new perspectives for solving the most complex problems of modern civilization and new challenges in the technological, ontological, and anthropological dimensions.

This study aims to develop AI․SW liberal arts subjects for university learners based on the analysis of learners' needs for artificial intelligence and software education. To analyze learners' educational needs, a survey was conducted on learners at A University. Based on the analysis, the AI․SW liberal arts subjects was designed to combine learning theory of artificial intelligence and Python practice. The theory of artificial intelligence focuses on the development process and service cases to increase the interest of learners. It helps learners explore ways to use artificial intelligence in their field. And Python practice is to create results by modifying the source code to reduce the burden of beginners. In the long term, it helps learners integrate artificial intelligence into their majors. This study presents basic research for a liberal arts curriculum that creates talents who can converge artificial intelligence in each academic field.

Privatization began in the 1970s with Carter's deregulation of some business, and increased with the Thatcher administration in the United Kingdom, the Reagan administration in the United States, and many communist and socialist countries. One area of concern in privatization is transportation—airports, water ports, roads, and mass transit. Privatization can be implemented in financing, construction, operation, and maintenance of the transportation system, the main motives being the belief that the private sector can be more efficient than the public sector, and because public funds are becoming less plentiful for a variety of reasons. The focus is on ideas and innovations for expanding the private role in transportation. Specifically covered are ideas and innovations for expanding the role of private sector in U.S. transportation projects, private financing of urban transportation, airport privatization, water port improvement, toll roads, and competitive contracting for transit services. The distinguished list of contributors includes the co-recipient of the 1996 Nobel Prize in Economics, William Vickrey. The audience for the work are scholars dealing with the discussions concerning the economics and politics of privatization, business people who are likely to be interested in potential opportunities, governmental regulators and staff, and policy makers.

This chapter is devoted to briefly explaining Linear Programming (LP) and the SIMUS method. The first was developed in the mid-twentieth century (1940), during the WW2 to determine the best use of Russian resources to repel German invasion. It was so successful that its creators were awarded the Nobel Prize in Economics. It was the first rational method developed for decision-making, but it had serious difficulties in modelling problems because of its complexity.

In 1994, John Forbes Nash Jr received the Nobel Prize in Economics for a one-page note published in 1950. It introduced what today is called the Nash equilibrium, arguably the most basic concept, not just for game theory, but for every model of a social situation where the consequences, for each agent involved, depend not only on their own actions but also on those of the others. In a Nash equilibrium, none of the 'players' has an incentive to change strategy as long as the other players keep to theirs...

The Nobel Prize in Economics celebrates decades of research in specific fields that have helped to improve the fundamental understanding of the subject and in many cases led to applications for attaining superior conditions of life. Since many parallel ideas emerge during the course of research, the relevance and criticality of these in addressing core concerns of the human being and the nature gets prominence for the award of the greatest accolade. Understanding sources of financial crisis and policies to lessen its impacts is a natural candidate for Nobel Prize, as has been the case for the award in 2022.

The paper presents the Chicago School economist Theodore Schultz's contributions to economic theory. Schultz is the only agricultural economist to have been awarded the Nobel Prize in Economics. The first part briefly describes the life of Theodore Schultz. The second part deals with his works in the economics of agriculture. The third part discusses his book The Economic Value of Education (Schultz, 1963). The fourth part analyses his work Investing in People (Schultz, 1981). The fifth part explores the book The Economics of Being Poor (Schultz, 1993). The sixth part outlines the book Origin of Increasing Returns (Schultz, 1993). The seventh part discusses his Nobel Prize lecture.

This article reflects on the importance of the theory of mechanism design as a tool to achieve socially desirable goals. The overall purpose is to explain how this advanced topic of economics allows optimal allocations of resources when the exchange is subject to the existence of asymmetric information between the parties. First, we present the conceptual foundations of mechanism design and theoretical contributions by which Leonid Hurwicz, Eric Maskin and Roger Myerson were awarded the Nobel Prize in Economics in 2007. Secondly, we expose the methodological foundations of the theory. The literature shows that to guide transactions and achieve predetermined outcomes is necessary that the principal to implement institutional arrangements that induce agents to reveal their private information for reconciling their incentives.

The story of the Nobel Prize in economics for 1976 has entered history. The reasons officially presented by the committee need not be repeated. I had the honor and pleasure of describing Milton Friedman’s contributions to monetary theory, aggregative analysis, and statistical theory in Science at the time. They have been discussed by many others, and they are, in any case, so well known and so much in the mainstream of economics as to obviate further discussion at this time, in this place, and for this group.KeywordsSocial InstitutionSocial RealityPrice TheoryFinancial PolicyMetaphysical ViewThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.