Risk sensitive scheduling strategies of production studios on the US movie market: An agent-based simulation

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2022-07-08 DOI:10.3233/ia-210123
Francesco Bertolotti, S. Roman
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引用次数: 2

Abstract

The movie industry is a highly differentiated context where production studios compete in non-price product attributes, which influences the box office results of a motion picture. Because of the short life cycle and the constant entrance of new competitive products, temporal decisions play a crucial role. Time series of the number of movies on release and the sum of the box office results of the ten top motion pictures (ranked by box office result for that week) present a counterphased seasonality in the US movie market. We suggest that a possible reason is a risk sensitivity adaptation in the behaviour of the movie’s distributors. This paper provides a model supporting this hypothesis. We developed an agent-based model of a movie market, and we simulated it for 15 years. A comparable global behaviour exists when producers schedule the movies according to given risk-sensitive strategies. This research improves the knowledge of the US motion picture market, analyzing a real-world scenario and providing insight into the behaviour of existing firms in a complex environment.
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美国电影市场制片公司的风险敏感调度策略:基于代理的模拟
电影行业是一个高度差异化的环境,制片公司在非价格产品属性上竞争,这影响了电影的票房结果。由于企业生命周期短,新的竞争产品不断进入市场,因此时间决策起着至关重要的作用。上映电影数量的时间序列和前十部电影的票房结果之和(按当周票房结果排序)在美国电影市场上呈现出一种反向的季节性。我们认为一个可能的原因是电影发行商行为中的风险敏感性适应。本文提供了一个支持这一假设的模型。我们开发了一个基于主体的电影市场模型,并对其进行了15年的模拟。当制片人根据给定的风险敏感策略安排电影时,就存在类似的全球行为。这项研究提高了对美国电影市场的认识,分析了现实世界的场景,并提供了对现有公司在复杂环境中的行为的见解。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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