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.
Auctions are increasingly being considered as a mechanism for allocating conservation contracts to private landowners. This interest is based on the widely held belief that competitive bidding helps minimize information rents. This study constructs an agent-based model to evaluate the long-term performance of conservation auctions under settings where bidders are allowed to learn from previous outcomes. The results clearly indicate that the efficiency benefits of one-shot auctions are quickly eroded under dynamic settings. Furthermore, the auction mechanism is not found to be superior to fixed payment schemes except when the latter involve the use of high prices.
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.
Auctions are increasingly being considered as a mechanism for allocating conservation contracts to private landowners. This
interest is based on the widely held belief that competitive bidding helps minimize information rents. This study constructs
an agent-based model to evaluate the long term performance of conservation auctions under settings where bidders are allowed
to learn from previous outcomes. The results clearly indicate that the efficiency benefits of one-shot auctions are quickly
eroded under dynamic settings. Furthermore, the auction mechanism is not found to be superior to fixed payment schemes except
when the latter involve the use of high prices.
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.
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.
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.
Some segregation results from the practices of organizations, some from specialized communication systems, some from correlation with a variable that is non‐random; and some results from the interplay of individual choices. This is an abstract study of the interactive dynamics of discriminatory individual choices. One model is a simulation in which individual members of two recognizable groups distribute themselves in neighborhoods defined by reference to their own locations. A second model is analytic and deals with compartmented space. A final section applies the analytics to ‘neighborhood tipping.’ The systemic effects are found to be overwhelming: there is no simple correspondence of individual incentive to collective results. Exaggerated separation and patterning result from the dynamics of movement. Inferences about individual motives can usually not be drawn from aggregate patterns. Some unexpected phenomena, like density and vacancy, are generated. A general theory of ‘tipping’ begins to emerge.
Agent-based modeling can illuminate how complex marketing phenomena emerge from simple decision rules. Marketing phenomena that are too complex for conventional analytical or empirical approaches can often be modeled using this approach. Agent-based modeling investigates aggregate phenomena by simulating the behavior of individual “agents,” such as consumers or organizations. Some useful examples of agent-based modeling have been published in marketing journals, but widespread acceptance of the agent-based modeling method and publication of this method in the highest-level marketing journals have been slowed by the lack of widely accepted standards of how to do agent-based modeling rigorously. We address this need by proposing guidelines for rigorous agent-based modeling. We demonstrate these guidelines, and the value of agent-based modeling for marketing research, through the use of an example. We use an agent-based modeling approach to replicate the Bass model of the diffusion of innovations, illustrating the use of the proposed guidelines to ensure the rigor of the analysis. We also show how extensions of the Bass model that would be difficult to carry out using traditional marketing research techniques are possible to implement using a rigorous agent-based approach.
Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.
In this paper, the autor extends his previous treatment of «The Bargaining Problem» to a wider class of situations in which threats can play a role/ A new approach is introduced involving the elaboration of the threat concept.