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Towards Agent-based Simulation of the Parallel Trading Market of Pharmaceuticals
Abstract
Pharmaceutical parallel trade is a legal trade in European countries, where traders can buy medicinal products in one country and sell them in other countries to make a profit. In the pharmaceutical parallel trade market, players such as manufacturers, wholesalers, parallel traders, pharmacies, and hospitals are involved. Studying and analyzing this market is of significant interest to economists and players involved. Agent-based modeling offers a robust algorithmic framework to analyze macroeconomic phenomena through micro-founded models. As an initial step in using agent-based modeling for the parallel trade of pharmaceuticals, we consider a simplified pharmaceutical trading market inspired by available game theory models. In this paper, we developed and elaborated the implementation of an agent-based model for the pharmaceutical trade market and employed it to run multiple scenarios that are impossible to analyze through game-theoretic models. Subsequently, we demonstrated how an agent-based model could be utilized to analyze the market from an economic perspective and how players in this market can recruit this model in their business decisions.
The European pharmaceutical parallel trade refers to the practice of purchasing pharmaceutical products in one European Union (EU) member state at a lower price and reselling the products in another EU member state at a higher price. In the pharmaceutical market, pricing strategies are of utmost importance as the market structure and regulations allow only the lowest-priced product to gain market share, making it imperative for players to optimize their pricing decisions in order to remain competitive. Therefore, developing a dynamic and data-driven pricing strategy that takes into account market conditions, competitors’ behaviors, and regulatory compliance is of interest to players involved in this market. In this paper, we demonstrate the potential of agent-based modeling as a tool for integrating mathematical modeling and economic concepts and investigating targeted pricing strategies in the pharmaceutical parallel trade market. We achieve this by utilizing agent-based modeling to evaluate and compare multiple pricing strategies through simulation. We aim to identify the challenges associated with developing a dynamic pricing approach in this complex market by showcasing the effectiveness of agent-based modeling. We contribute to the understanding of pricing strategies and their implications in the pharmaceutical parallel trade market.KeywordsAgent-based modeling and simulationPricing strategyPrice competitionPharmaceutical parallel trade
Pharmaceutical parallel trade emerged due to the European Union's single market for medicines. While many players, such as manufacturers, wholesalers, parallel traders, pharmacies, regulatory authorities, and hospitals, are involved in this market, having a model that accurately reflects the parallel trade market could be a considerable advantage for players in this market. One way to model the parallel trade market is by employing game theory, which is frequently used to model and explain business interactions. However, game theory imposes limitations on models. Agent-based modeling is a promising framework for studying the parallel trade market, which allows us to investigate macroscopic outcomes that emerge from microscopic rules, decisions, and interactions. Moreover, agent-based modeling allows for high expressiveness and complexity in agents, improving agents' efficiency in autonomy and reactivity compared to current game theoretic models. In this paper, we aim to build an agent-based model for the pharmaceutical parallel trading market based on the available game-theoretic model of the market.
This work nests the Agent-Based macroeconomic perspective into the earlier history of macroeconomics. We discuss how the discipline in the 70’s took a perverse path relying on models grounded on fictitious rational representative agent in order to try to pathetically circumvent aggregation and coordination problems. The Great Recession was a natural experiment for macroeconomics, showing the inadequacy of the predominant theoretical framework grounded on DSGE models. After discussing the pathological fallacies of the DSGE-based approach, we claim that macroeconomics should consider the economy as a complex evolving system, i.e. as an ecology populated by heterogenous agents, whose far-from-equilibrium interactions continuously change the structure of the system. This in turn implies that more is different: macroeconomics cannot be shrink to representative-agent micro, but agents’ complex interactions lead to emergence of new phenomena and hierarchical structure at the macro level. This is what is taken into account by agent-based models, which provide a novel way to model complex economies from the bottom-up, with sound empirically-based microfoundations. We present the foundations of Agent-Based macroeconomics and we discuss how the contributions of this special issue push its frontier forward. Finally, we conclude by discussing the ways ahead for the fully acknowledgement of agent-based models as the standard way of theorizing in macroeconomics.
This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.
The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). DS is of importance in a discipline oriented to the creation of successful artifacts. Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.
Epidemics have disturbed human lives for centuries causing massive numbers of deaths and illnesses among people and animals. As the number of urbanized and mobile population has increased, the possibility of a worldwide pandemic has grown too. The latest advances in high-performance computing and computational network science can help computational epidemiologists to develop large-scale high-fidelity models of epidemic spread. These models can help to characterize the large-scale patterns of epidemics and guide public health officials and policy makers in taking appropriate decisions to prevent and control such epidemics. This paper presents an overview of the epidemic spread modeling and simulation, and summarizes the main technical challenges in this field. It further investigates the most relevant recent approaches carried out towards this perspective and provides a comparison and classification of these approaches.
Price controls create opportunities for international arbitrage. Many have argued that such arbitrage, if tolerated, will undermine intellectual property rights and dull the incentives for investment in research-intensive industries such as pharmaceuticals. We challenge this orthodox view and show, to the contrary, that the pace of innovation often is faster in a world with international exhaustion of intellectual property rights than in one with national exhaustion. The key to our conclusion is to recognize that governments will make different choices of price controls when parallel imports are allowed by their trade partners than they will when they are not.
Most existing studies on parallel trade conclude that it reduces pharmaceutical firms' profits. One special feature of the pharmaceutical industry is the presence of price regulation in most countries. Taking into account the impact of parallel trade on the regulated pharmaceutical prices [Pecorino, P.: J. Health Econ. 21, 699-708 (2002)] shows that a pharmaceutical firm's profit is greater in the presence of parallel trade. The present paper relaxes the assumption on identical demands among countries, and takes into account transaction costs. The results of our model show that a firm's profits may increase or decrease in the presence of parallel trade, depending on its bargaining power in the price negotiation and market size of the drug. Changes in social welfare due to the transition to parallel trade regime are also considered.
Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far reaching effects on the way that businesses use computers to support decision making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agent-based applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of biowarfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.
The paper motivates, presents, demonstrates in use, and evaluates
a methodology for conducting design science (DS) research in information
systems (IS). DS is of importance in a discipline oriented to the
creation of successful artifacts. Several researchers have pioneered
DS research in IS, yet over the past 15 years, little DS research
has been done within the discipline. The lack of a methodology to
serve as a commonly accepted framework for DS research and of a template
for its presentation may have contributed to its slow adoption. The
design science research methodology (DSRM) presented here incorporates
principles, practices, and procedures required to carry out such
research and meets three objectives: it is consistent with prior
literature, it provides a nominal process model for doing DS research,
and it provides a mental model for presenting and evaluating DS research
in IS. The DS process includes six steps: problem identification
and motivation, definition of the objectives for a solution, design
and development, demonstration, evaluation, and communication. We
demonstrate and evaluate the methodology by presenting four case
studies in terms of the DSRM, including cases that present the design
of a database to support health assessment methods, a software reuse
measure, an Internet video telephony application, and an IS planning
method. The designed methodology effectively satisfies the three
objectives and has the potential to help aid the acceptance of DS
research in the IS discipline.
The sharing economy is a peer-to-peer economic model characterized by people and organizations sharing resources. With the emergence of such economies, an increasing number of logistics providers seek to collaborate and derive benefit from the resultant economic efficiencies, sustainable operations, and network resilience. This study investigates the potential for collaborative planning enabled through a Physical Internet-enabled logistics system in an urban area that acts as a freight transport hub with several e-commerce warehouses. Our collaborative freight transportation planning approach is realized through a three-layer structured hybrid model that includes agent-based simulation, auction mechanism, and optimization. A multi-agent model simulates a complex transportation network, an auction mechanism facilitates allocating transport services to freight requests, and a simulation–optimization technique is used to analyze strategic transportation planning under different objectives. Furthermore, sensitivity analyses and Pareto efficiency experiments are conducted to draw insights regarding the effect of parameter settings and multi-objectives. The computational results demonstrate the efficacy of our developed model and solution approach, tested on a real urban freight transportation network in a major US city.
Game theory in biology seeks to predict social behaviour and other traits that influence how individuals interact. It does this by tentatively assuming that current traits are stable endpoints of evolution by natural selection. The theory is used to model aggressive behaviour, cooperation, negotiation, and signalling, as well as phenotypic attributes like an individual’s sex and mating type. This book covers the basic concepts and the traditional examples of biological game theory. It expands the frontiers of the field, emphasizing the importance of the co-evolution of traits and the implications of variation for reputation, markets, negotiation, and other social phenomena. It also highlights that it can be important to embed game interactions in the environment and an individual’s life. A major new direction developed in the book is that game theory can be extended by incorporating behavioural mechanisms, including mechanisms of reinforcement learning. By doing this the theory can successfully describe important phenomena like social dominance in group-living animals that previously have been difficult to model. By focusing on behavioural mechanisms, game theory can also make closer contact with empirical observation and with current research in fields like animal psychology and neuroscience.
This chapter surveys work dedicated to macroeconomic analysis using an agent-based modeling approach. After a short review of the origins and general characteristics of this approach a systemic comparison of the structure and modeling assumptions of a set of important (families of) agent-based macroeconomic models is provided. The comparison highlights substantial similarities between the different models, thereby identifying what could be considered an emerging common core of macroeconomic agent-based modeling. In the second part of the chapter agent-based macroeconomic research in different domains of economic policy is reviewed.
This essay examines game theory’s transformation to becoming the major tool in economics, arguing that the initial excitement surrounding game theory has dissipated not because game theory has retreated from its initial bridgehead, but because it has extended its reach throughout economics. Next, it discusses some key challenges for game theory, including the continuing problem of dealing with multiple equilibria, the need to make game theory useful in applications, and the need to better integrate noncooperative and cooperative game theory. Finally it considers the current status and future prospects of game theory.
Importance: While systemic therapy for disseminated cancer is often initially successful, malignant cells, using diverse adaptive strategies encoded in the human genome, almost invariably evolve resistance, leading to treatment failure. Thus, the Darwinian dynamics of resistance are formidable barriers to all forms of systemic cancer treatment but rarely integrated into clinical trial design or included within precision oncology initiatives.
Observations: We investigate cancer treatment as a game theoretic contest between the physician’s therapy and the cancer cells’ resistance strategies. This game has 2 critical asymmetries: (1) Only the physician can play rationally. Cancer cells, like all evolving organisms, can only adapt to current conditions; they can neither anticipate nor evolve adaptations for treatments that the physician has not yet applied. (2) It has a distinctive leader-follower (or “Stackelberg”) dynamics; the “leader” oncologist plays first and the “follower” cancer cells then respond and adapt to therapy. Current treatment protocols for metastatic cancer typically exploit neither asymmetry. By repeatedly administering the same drug(s) until disease progression, the physician “plays” a fixed strategy even as the opposing cancer cells continuously evolve successful adaptive responses. Furthermore, by changing treatment only when the tumor progresses, the physician cedes leadership to the cancer cells and treatment failure becomes nearly inevitable. Without fundamental changes in strategy, standard-of-care cancer therapy typically results in “Nash solutions” in which no unilateral change in treatment can favorably alter the outcome.
Conclusions and Relevance: Physicians can exploit the advantages inherent in the asymmetries of the cancer treatment game, and likely improve outcomes, by adopting more dynamic treatment protocols that integrate eco-evolutionary dynamics and modulate therapy accordingly. Implementing this approach will require new metrics of tumor response that incorporate both ecological (ie, size) and evolutionary (ie, molecular mechanisms of resistance and relative size of resistant population) changes.
Within the EU Single Market for medicines, differences in drug prices, regulations, and transaction costs may create, under suitable conditions, arbitrage opportunities well before patent expiration, giving an incentive to the occurrence of parallel trade. When this is permitted, parallel traders may obtain a profit from buying drugs in a country where prices are lower, then re-selling them in a country where prices are higher. This phenomenon may cause inefficiencies from a global welfare perspective, and reduce the manufacturers’ incentive to invest in Research and Development (R & D). Given this framework, in this paper, we investigate the efficiency (expressed in terms of the price of anarchy) of the subgame-perfect Nash equilibria associated with five dynamic noncooperative game-theoretic models for the parallel trade of pharmaceuticals. We also compare such models with regard to the manufacturer’s incentive to invest in R & D. More specifically, first we find in closed form the optimal value of the global welfare of two countries, which is obtained by solving a suitable quadratic optimization problem modeling the decision-making process of a global planner. Then, we use such a result to evaluate and compare the prices of anarchy of five games modeling the interaction between a manufacturer in the first country and a potential parallel trader in the second country. The first three games refer, respectively, to the cases of no parallel trade threat, parallel trade threat, and parallel trade occurrence at equilibrium. Then, we investigate two modifications of the third game, in which its transfer payment from the potential parallel trader to the manufacturer is, respectively, removed/determined by Nash bargaining. For completeness, we also consider a decision-theoretic model of no parallel trade threat. For what concerns the incentive for the manufacturer to invest in R & D, the results of our numerical comparison show that the decision-theoretic model of no parallel trade threat is always the one with the highest incentive, whereas the two game-theoretic models of parallel trade threat/occurrence that do not include the transfer payment provide typically the lowest incentives. Moreover, the latter two models have the highest prices of anarchy (i.e., their equilibria have the lowest efficiencies). From a policy-making perspective, improvements are obtained if suitable countermeasures are taken to help the manufacturer recover from the costs of R & D, such as the inclusion of a transfer payment in the model.
Within economics, game theory occupied a rather isolated niche in the 1960s and 1970s. It was pursued by people who were known specifically as game theorists and who did almost nothing but game theory, while other economists had little idea what game theory was. Game theory is now a standard tool in economics. Contributions to game theory are made by economists across the spectrum of fields and interests, and economists regularly combine work in game theory with work in other areas. Students learn the basic techniques of game theory in the first-year graduate theory core. Excitement over game theory in economics has given way to an easy familiarity. This essay first examines this transition, arguing that the initial excitement surrounding game theory has dissipated not because game theory has retreated from its initial bridgehead, but because it has extended its reach throughout economics. Next, it discusses some key challenges for game theory, including the continuing problem of dealing with multiple equilibria, the need to make game theory useful in applications, and the need to better integrate noncooperative and cooperative game theory. Finally it considers the current status and future prospects of game theory.
Agent-based modeling and simulation (ABMS) is a relatively new approach to modeling systems composed of autonomous, interacting agents. Agent-based modeling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modeling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.
NOTE: The final published version and Python sample codes are now available from the following site. Please check it out!
http://bingweb.binghamton.edu/~sayama/textbook/
Agent-based modeling is a new technique for understanding how the dynamics of biological, social, and other complex systems arise from the characteristics and behaviors of the agents making up these systems. This innovative textbook gives students and scientists the skills to design, implement, and analyze agent-based models. It starts with the fundamentals of modeling and provides an introduction to NetLogo, an easy-to-use, free, and powerful software platform. Nine chapters then each introduce an important modeling concept and show how to implement it using NetLogo. The book goes on to present strategies for finding the right level of model complexity and developing theory for agent behavior, and for analyzing and learning from models. Agent-Based and Individual-Based Modelingfeatures concise and accessible text, numerous examples, and exercises using small but scientific models. The emphasis throughout is on analysis--such as software testing, theory development, robustness analysis, and understanding full models--and on design issues like optimizing model structure and finding good parameter values. The first hands-on introduction to agent-based modeling, from conceptual design to computer implementation to parameterization and analysis Filled with examples and exercises, with updates and supplementary materials at www.railsback-grimm-abm-book.com Designed for students and researchers across the biological and social sciences Written by leading practitioners.
The Theory of Industrial Organization is the first primary text to treat the new industrial organization at the advanced-undergraduate and graduate level. Rigorously analytical and filled with exercises coded to indicate level of difficulty, it provides a unified and modern treatment of the field with accessible models that are simplified to highlight robust economic ideas while working at an intuitive level. To aid students at different levels, each chapter is divided into a main text and supplementary section containing more advanced material. Each chapter opens with elementary models and builds on this base to incorporate current research in a coherent synthesis. Tirole begins with a background discussion of the theory of the firm. In part I he develops the modern theory of monopoly, addressing single product and multi product pricing, static and intertemporal price discrimination, quality choice, reputation, and vertical restraints. In part II, Tirole takes up strategic interaction between firms, starting with a novel treatment of the Bertrand-Cournot interdependent pricing problem. He studies how capacity constraints, repeated interaction, product positioning, advertising, and asymmetric information affect competition or tacit collusion. He then develops topics having to do with long term competition, including barriers to entry, contestability, exit, and research and development. He concludes with a "game theory user's manual" and a section of review exercises.
A Course in Game Theory presents the main ideas of game theory at a level suitable for graduate students and advanced undergraduates, emphasizing the theory's foundations and interpretations of its basic concepts. The authors provide precise definitions and full proofs of results, sacrificing generalities and limiting the scope of the material in order to do so. The text is organized in four parts: strategic games, extensive games with perfect information, extensive games with imperfect information, and coalitional games. It includes over 100 exercises.
How do social structures and group behaviors arise from the interaction of individuals? Growing Artificial Societies approaches this question with cutting-edge computer simulation techniques. Fundamental collective behaviors such as group formation, cultural transmission, combat, and trade are seen to "emerge" from the interaction of individual agents following a few simple rules. In their program, named Sugarscape, Epstein and Axtell begin the development of a "bottom up" social science that is capturing the attention of researchers and commentators alike. The study is part of the 2050 Project, a joint venture of the Santa Fe Institute, the World Resources Institute, and the Brookings Institution. The project is an international effort to identify conditions for a sustainable global system in the next century and to design policies to help achieve such a system.
As a result of public outrage over lower prescription drug prices in Canada, Congress passed legislation that would allow these drugs to be imported into the US. The lower Canadian prices reflect price regulation. Opponents of allowing these imports have argued that the US will import Canadian price controls and that profits of pharmaceutical companies will be hurt. In this paper, a model is developed in which a good sold in the foreign country is subject to a negotiated price which is determined in a Nash bargaining game. When imports back into the home country are allowed, this negotiated price also becomes the domestic price. This causes the home firm to make fewer price concession in the Nash bargaining game. Home firm profits are found to rise under the reimport regime for both of the demand functions analyzed in this paper.
We consider policy issues regarding parallel imports (PIs) of brand-name pharmaceuticals in the European Union, where such trade is permitted. We develop a simple model in which an original manufacturer competes in its home market with PI firms. The model suggests that for small trade costs the original manufacturer will accommodate the import decisions of parallel traders and that the price in the home market falls as the volume of parallel imports rises. Using data from Sweden we find that the prices of drugs subject to competition from parallel imports fell relative to other drugs over the period 1994-1999. Econometric analysis finds that parallel imports significantly reduced manufacturing prices, by 12-19%. There is evidence that this effect increases with multiple PI entrants.
We study the effects of parallel trade in the pharmaceutical industry. We develop a model in which an original manufacturer competes in its home market with parallel-importing firms. The theoretical analysis results in two key hypotheses. First, if the potential for parallel imports is unlimited, the manufacturer chooses deterrence and international prices converge. Second, with endogenously limited arbitrage the manufacturing firm accommodates and the price in the home market falls as the volume of parallel trade rises. Simple empirical tests favor the accommodation hypothesis with a time lag.
A survey on applications of game theory in blockchain
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Agent-based modeling in economics and finance: Past, present, and future
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Optimizing cancer treatment using game theory: A review
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