The Role Of Industry Structure On Customer Value In Robotic Surgery

Spending on robot surgery is expected to increase by $17 billion in the next 6 years. This new surgical treatment has challenged hospitals with higher costs and varying performance. Healthcare executives struggle balancing the adoption of medical innovations with managing healthcare costs. This dilemma can be further complicated by industry structures relative to capital-intensive medical innovations. This research explores the interaction between industry structure and customer value. Specifically, how can hospitals apply an understanding of supplier industry structure and customer value to improve the value of a robotic surgery program (RSP)? This industry study represents an exhaustive longitudinal review of over 15 years of public data relative to robotic surgery, across three distinct time periods. Within the research, industry structure is evaluated using Porter’s 5-forces model. A framework based upon contributions from Grönroos as well as Menon, Homburg, and Beutin is introduced to assess customer value based upon clinical, financial and strategic (CFS) value. The implications of periodic industry structure on customer value were examined to identify opportunities for hospital executives to increase RSP customer value. There were several empirical and theoretical findings from this research. First, in the face of increasing industry structure the identification of favorable forces may create opportunities to increase RSP value. Secondarily, exploring customer value through the lens of core, add-on, relational and transactional benefits in the sub-context of CFS value aids in the identification of market power influences on customer value. The implications of the absence of high levels of relational and transactional benefits without high levels of core and add-on benefits may influence avenues of pursuit in improving RSP value overall. The research also suggests that clinical and strategic value was present despite varying degrees of industry structure. Finally, this study represents an empirical joint analysis of industry structure and customer value in robotic surgery. Some proponents may find the introduction of an integrative model for measuring customer value in robotic surgery, applicable to other capital-intensive medical innovations or disruptive technologies at large

Adaptive-Aggressive Traders Don't Dominate

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently out-performed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.Comment: To be published as a chapter in "Agents and Artificial Intelligence" edited by Jaap van den Herik, Ana Paula Rocha, and Luc Steels; forthcoming 2019/2020. 24 Pages, 1 Figure, 7 Table

Production Networks Linkages, Innovation Processes and Social Management Technologies. A Methodological Approach Applied to the Volkswagen case in Argentina

The purpose of this paper -as a part of a wider research project - is to analyze the concept of production network from a methodological and theoretical viewpoint based on a three-plane perspective. These dimensions are the linkages among agents, the innovation activities, and the social management technology, including work process organization and the social agreement generation model in force. It is an experimentally methodological approach that tries to go from a theoretical conceptualization of the phenomenon to its empirical evaluation. The questions guiding this research are as follows: What are the variables and dimensions to be observed in the analysis of a group of interconnected firms in order to define a production network? Is it a unique definition or, on the contrary, does it involve a range of alternatives? What are the externalities generated by the agents who belong to one network? What is the relationship between the network’s firms’ technological behavior and their organizational counterpart? How are learning processes in the business firms linked to their own training systems? Has the social management technology some differential role in the learning process and in the development of skills? How do knowledge transmission processes manifest themselves within the “network”? What indicators are useful for the empirical identification of the different means of manifestation of the network according to the theoretical viewpoint adopted? How can those indicators be articulated in order to elaborate typologies intended for the identification of “hybrid” models? How can a complex indicator be built in order to show the different levels of circulation of intangible assets, development of learning processes and work process organization? In the first section, the conceptualization of the production “network” used in this paper is discussed. In the second section, most relevant variables and indicators are presented in order to feature the business firms and the network in terms of: a) type, quantity and quality of tangible and intangible exchanges among the agents; b) innovative capacity and learning; c) social management technology. Then we elaborate a typology of networks based on the consideration of the previous parameters. Lastly, in the fourth section, we discuss how the three dimensions interact in the case of Volkswagen and his forty main local suppliers.Innovation, production process, case study

Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order

This paper presents novel results generated from a new simulation model of a contemporary financial market, that cast serious doubt on the previously widely accepted view of the relative performance of various well-known public-domain automated-trading algorithms. Various public-domain trading algorithms have been proposed over the past 25 years in a kind of arms-race, where each new trading algorithm was compared to the previous best, thereby establishing a "pecking order", i.e. a partially-ordered dominance hierarchy from best to worst of the various trading algorithms. Many of these algorithms were developed and tested using simple minimal simulations of financial markets that only weakly approximated the fact that real markets involve many different trading systems operating asynchronously and in parallel. In this paper we use BSE, a public-domain market simulator, to run a set of experiments generating benchmark results from several well-known trading algorithms. BSE incorporates a very simple time-sliced approach to simulating parallelism, which has obvious known weaknesses. We then alter and extend BSE to make it threaded, so that different trader algorithms operate asynchronously and in parallel: we call this simulator Threaded-BSE (TBSE). We then re-run the trader experiments on TBSE and compare the TBSE results to our earlier benchmark results from BSE. Our comparison shows that the dominance hierarchy in our more realistic experiments is different from the one given by the original simple simulator. We conclude that simulated parallelism matters a lot, and that earlier results from simple simulations comparing different trader algorithms are no longer to be entirely trusted.Comment: 6 pages, 2 tables, 3 figures, to be presented at European Modelling and Simulation Symposium (EMSS2020

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Technology adoption and the organization of production. The case of digital production technologies

open1noopenguendalina anzolinAnzolin, GUENDALINA MARI

Endogenous games with goals : side-payments among goal-directed agents

Boolean games have been developed as a paradigm for modelling societies of goal-directed agents. In boolean games agents exercise control over propositional variables and strive to achieve a goal formula whose realization might require the opponents’ cooperation. The presence of agents that are goal-directed makes it difficult for an external authority to be able to remove undesirable properties that are inconsistent with agents’ goals, as shown by recent contributions in the multi-agent literature. What this paper does is to analyse the problem of regulation of goal-direct agents from within the system, i.e., what happens when agents themselves are given the chance to negotiate the strategies to be played with one another. Concretely, we introduce endogenous games with goals, obtained coupling a general model of goal-directed agents (strategic games with goals) with a general model of pre-play negotiations (endogenous games) coming from game theory. Strategic games with goals are shown to have a direct correspondence with strategic games (Proposition 1) but, when side-payments are allowed in the pre-play phase, display a striking imbalance (Proposition 4). The effect of side-payments can be fully simulated by taxation mechanisms studied in the literature (Proposition 7), yet we show sufficient conditions under which outcomes can be rationally sustained without external intervention (Proposition 5). Also, integrating taxation mechanisms and side-payments, we are able to transform our starting models in such a way that outcomes that are theoretically sustainable thanks to a pre-play phase can be actually sustained even with limited resources (Proposition 8). Finally, we show how an external authority incentivising a group of agents can be studied as a special agent of an appropriately extended endogenous game with goals (Proposition 11)