Impact of artificial intelligence investment on firm value

As artificial intelligence (AI) has recently gained momentum and attention, the interest and investment in AI have also accelerated. However, the impact of AI on firm value is rarely discussed. On the basis of the 119 announcements of 62 listed firms who have invested in AI, this study finds that AI investment has a negative impact on the firms’ market value. The stock prices of the firms decrease by 1.77% on the day of the announcement. Nonmanufacturing firms and firms with weak information technology capabilities or low credit ratings suffer a more negative impact compared with other firms. The findings suggest that investors perceive AI investment announcements to be unwelcome news for the majority of firms. Subsequently, the characteristics affecting the shareholders’ reaction towards AI adoption are presented. This research offers one of the first empirical evidence about the market value of AI and provides a reference for firms interested in investing in AI.

     

Impact of artificial intelligence adoption on online returns policies

The shift to e-commerce has led to an astonishing increase in online sales for retailers. However, the number of returns made on online purchases is also increasing and have a profound impact on retailers’ operations and profit. Hence, retailers need to balance between minimizing and allowing product returns. This study examines an offline showroom versus an artificial intelligence (AI) online virtual-reality webroom and how the settings affect customers’ purchase and retailers’ return decisions. A case study is used to illustrate the AI application. Our results show that adopting artificial intelligence helps sellers to make better returns policies, maximize reselling returns, and reduce the risks of leftovers and shortages. Our findings unlock the potential of artificial intelligence applications in retail operations and should interest practitioners and researchers in online retailing, especially those concerned with online returns policies and the consumer personalized service experience.

     

Appraisals of harms and injustice trigger an eerie feeling that decreases trust in artificial intelligence systems

As artificial intelligence (AI) becomes more pervasive, the concern over how users can trust artificial agents is more important than ever before. In this research, we seek to understand the trust formation between humans and artificial agents from the morality and uncanny theory perspective. We conducted three studies to carefully examine the effect of two moral foundations: perceptions of harm and perceptions of injustice, as well as reported wrongdoing on uncanniness and examine the effect of uncanniness on trust in artificial agents. In Study 1, we found perceived injustice was the primary determinant of uncanniness and uncanniness had a negative effect on trust. Studies 2 and 3 extended these findings using two different scenarios of wrongful acts involving an artificial agent. In addition to explaining the contribution of moral appraisals to the feeling of uncanny, the latter studies also uncover substantial contributions of both perceived harm and perceived injustice. The results provide a foundation for establishing trust in artificial agents and designing an AI system by instilling moral values in it.

     

Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

     

Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research

The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.

     

Artificial intelligence-based inventory management: a Monte Carlo tree search approach

The coordination of order policies constitutes a great challenge in supply chain inventory management as various stochastic factors increase its complexity. Therefore, analytical approaches to determine a policy that minimises overall inventory costs are only suitable to a limited extent. In contrast, we adopt a heuristic approach, from the domain of artificial intelligence (AI), namely, Monte Carlo tree search (MCTS). To the best of our knowledge, MCTS has neither been applied to supply chain inventory management before nor is it yet widely disseminated in other branches of operations research. We develop an offline model as well as an online model which bases decisions on real-time data. For demonstration purposes, we consider a supply chain structure similar to the classical beer game with four actors and both stochastic demand and lead times. We demonstrate that both the offline and the online MCTS models perform better than other previously adopted AI-based approaches. Furthermore, we provide evidence that a dynamic order policy determined by MCTS eliminates the bullwhip effect.

     

Adoption and use of AI tools: a research agenda grounded in UTAUT

This paper is motivated by the widespread availability of AI tools, whose adoption and consequent benefits are still not well understood. As a first step, some critical issues that relate to AI tools in general, humans in the context of AI tools, and AI tools in the context of operations management are identified. A discussion of how these issues could hinder employee adoption and use of AI tools is presented. Building on this discussion, the unified theory of acceptance and use of technology is used as a theoretical basis to propose individual characteristics, technology characteristics, environmental characteristics and interventions as viable research directions that could not only contribute to the adoption literature, particularly as it relates to AI tools, but also, if pursued, such research could help organizations positively influence the adoption of AI tools.

     

Using genetic algorithms to optimize nearest neighbors for data mining

Case-based reasoning (CBR) is widely used in data mining for managerial applications because it often shows significant promise for improving the effectiveness of complex and unstructured decision making. There are, however, some limitations in designing appropriate case indexing and retrieval mechanisms including feature selection and feature weighting. Some of the prior studies pointed out that finding the optimal k parameter for the k-nearest neighbor (k-NN) is also one of the most important factors for designing an effective CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. This study proposes a genetic algorithm (GA) approach to optimize the number of neighbors to combine. In this study, we apply this novel model to two real-world cases involving stock market and online purchase prediction problems. Experimental results show that a GA-optimized k-NN approach may outperform traditional k-NN. In addition, these results also show that our proposed method is as good as or sometime better than other AI techniques in performance-comparison.     

On the operations of sequences in rings and binomial type sequences

Given a commutative ring with identity R, many different and interesting operations can be defined over the set \(H_R\) of sequences of elements in R. These operations can also give \(H_R\) the structure of a ring. We study some of these operations, focusing on the binomial convolution product and the operation induced by the composition of exponential generating functions. We provide new relations between these operations and their invertible elements. We also study automorphisms of the Hurwitz series ring, highlighting that some well-known transforms of sequences (such as the Stirling transform) are special cases of these automorphisms. Moreover, we introduce a novel isomorphism between \(H_R\) equipped with the componentwise sum and the set of the sequences starting with 1 equipped with the binomial convolution product. Finally, thanks to this isomorphism, we find a new method for characterizing and generating all the binomial type sequences.

     

Aftermath of the Chernobyl Catastrophe from the Point of View of the Security Concept

The paper deals with uncertainty relations for time and energy operators, and the aftermath of the Chernobyl catastrophe is considered as an example. The mathematical approach developed by Holevo is analyzed, which allows us to assign the corresponding observables to non-self-adjoint operators and to establish uncertainty relations for nonstandard canonical conjugate pairs.

Relations for calculating the minimal time interval in which the energy jump can be discovered are given. Based on the intensity parameter introduced by the author, which is related to a special statistics called Gentile statistics and to the polylogarithm function, properties of stable chemical elements, such as time fluctuations and the jump of specific energy in the transition from the Bose—Einstein distribution to the Fermi—Dirac distribution, are mathematically described with regard to experimental data. The obtained data are arranged in a table for 255 stable chemical elements.

The mathematical approach developed by the author of the present paper allows one to describe the “antipode” (in a certain sense) of the standard thermodynamics, i.e., the thermodynamics of nuclear matter. This field of nuclear physics is very important for the study of properties of radioactive elements and, accordingly, from the standpoint of ensuring nuclear safety.

     

Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as ‘knowledge-based improvement’ (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.     

Artificial intelligence and social forecasting*

Artificial intelligence (AI) is the study of how to write programs enabling computers to do things that would require intelligence if done by people, and it could engage with social forecasting in two ways. First, it is part of the overall social‐technological context within which forecasters work. Commercial Al‐programs will affect markets and life‐styles; and advice‐giving “expert” systems will raise novel legal, social, and psychological problems. Second, AI‐programs might be used for making the social forecasts. Unlike the (essentially quantitative) computer models used for this purpose today, they could reason (and explain themselves) in verbal form. Writing an expert system requires clarification of the theories, assumptions, and “rule‐of‐thumb” inferences concerned. It would be easier to identify the inherent moral‐political bias than it is in models comprising sets of differential equations.     

FUNCTIONAL IDENTITIES REVISED: THE FRACTIONAL AND THE STRONG DEGREE

ABSTRACT

The concepts of the fractional and the strong degree of an element in a ring are introduced. It is shown that definitive results on functional identities can be obtained in rings which contain elements of appropriate fractional (or strong) degree. This enables us to extend the results on functional identities from prime to semiprime rings, as well as to some rather different classes of rings, such as matrix rings over any unital ring. As an application, commuting maps, Lie derivations and commutativity preserving maps in such rings are discussed.     

Eléments analytiques et filtres percés sur un ensemble infraconnexe

Summary Let K be a complete ultrametric algebraically closed field, let D be an infraconnected bounded closed set of K and let H(D) be the Banach algebra of the analytical elements on D. The properties of the elements f of H(D) are learnt introducing a function v(f, μ) continuous and affine on pieces in the intervals where it is bounded. We learn the elements f ε H(D) which are not a product of a polynomial with an invertible element. We introduce the notion of monotonous filter, related with the continuous multiplicative semi-norms of H(D) and we prove these such elements are annulated by a monotonous filter and more precisely, a pierced monotonous filter.

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Entrata in Redazione il 13 settembre 1975.     

An Anthology on the Homology of Simulation with Artificial Intelligence

Simulation and artificial intelligence (AI) can be related in the following ways: the methodological similarities, the actual and potential uses of AI concepts in simulation, expert systems in simulation environments, and the gains for AI when applying ideas from simulation. This paper highlights the personal experiences of the author by discussing, for each of the four above-mentioned areas, a topic or a computer system in which he is directly involved. To highlight the similarities, it will be shown that the three-phase simulation model can be considered as a production-system model. A natural-language understanding system that was used in the definition stage of a simulation project will provide the basis for discussing AI concepts in simulation environments. An expert system used in the development and verification stages will provide an insight into the application of expert systems in simulation modelling. An expert-system development tool which is partially based on simulation techniques will be used to outline the gains for AI.     

Confident Search

Abstract

The task of searching for the best element or a good element in a large set P is central to many problems in artificial intelligence and related fields. Often, heuristic information is used to reduce the scope of the search; however, in many instances, this information carries no guarantee of good performance. This article begins with an arbitrary heuristic search procedure and supplies it with a confidence statement of the following form: With specified high probability β, the output of the confidence procedure will be among the best 100α% of the elements of P. The confidence procedure will report either the outcome of the heuristic search or a better alternative with the required properties; that is, it will either certify that the heuristic answer has the desired confidence property or it will produce a better answer having the property. The approach involves combining heuristic search with a form of heuristic sampling that tends to sample the better elements of P. The sample is designed in such a way that the best element in the sample has the desired confidence property—if the answer produced by the heuristic search is better still, it inherits the confidence property. Various devices permit the sampling procedure to retain its confidence property while (1) moving the sample in the direction suggested by the heuristic, (2) adjusting the heuristic preference in response to what is learned during sampling, and (3) reorganizing the sampling whenever promising discoveries are made by chance.     

Transforming business using digital innovations: the application of AI,blockchain, cloud and data analytics

This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD). Specifically, the study investigates the operations and value propositions of these distinct but increasingly converging technologies. Due to the dynamic nature of innovation, the potential of this ABCD hybridization, integration, recombination and convergence has yet to be considered. Using a multidisciplinary approach, the findings of the study show wide-reaching and diverse applications among a variety of vertical sectors, presenting exploratory research avenues for future investigation. The study also highlights the practical implications of these new technologies.

     

The Holt–Klee condition for oriented matroids

Holt and Klee have recently shown that every (generic) LP orientation of the graph of a d-polytope satisfies a directed version of the d-connectivity property, i.e. there are d internally disjoint directed paths from a unique source to a unique sink. We introduce two new classes HK and HK* of oriented matroids (OMs) by enforcing this property and its dual interpretation in terms of line shellings, respectively. Both classes contain all representable OMs by the Holt–Klee theorem. While we give a construction of an infinite family of non-HK* OMs, it is not clear whether there exists any non-HK OM. This leads to a fundamental question as to whether the Holt–Klee theorem can be proven combinatorially by using the OM axioms only. Finally, we give the complete classification of OM(4, 8), the OMs of rank 4 on 8-element ground set with respect to the HK, HK*, Euclidean and Shannon properties. Our classification shows that there exists no non-HK OM in this class.     

Dynamic construction of statistical models in managerial DSS

The fields of operations research (OR) and artificial intelligence (AI) provide complementary methods that may be combined into managerial decision support systems (DSS). However, the management domain is substantially different from domains in which prior expert systems have been developed. Consequently, successful application of OR/AI techniques in managerial DSS requires careful analysis and additional development. Ongoing research concerning design and implementation of managerial DSS is discussed. A prototype system capable of constructing linear statistical models of direct and indirect relationships from a knowledge base of relationships is described and evaluated.     

To lay out or not to lay out?

The Quadratic Assignment Problem (QAP) is known as one of the most difficult problems within combinatorial optimization. It is used to model many practical problems including different layout problems. The main topic of this paper is to provide methods to check whether a particular instance of the QAP is a layout problem. An instance is a layout problem if the distances of the objects can be reconstructed on the plane and/or in the 3-dimensional space. A new mixed integer programming model is suggested for the case if the distances of the objects are supposed to be rectilinear distances. If the distances are Euclidean distances then the use of the well-known Multi-Dimensional Scaling (MDS) method of statistics is suggested for reconstruction purposes. The well-known difficulty of QAP makes it a popular and suitable experimental field for many algorithmic ideas including artificial intelligence methods. These types of results are published sometimes as layout problems. The methods of reconstruction can be used to decide whether the topic of a paper is layout or only general QAP. The issue what the OR community should expect from AI based algorithms, is also addressed.