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Live fast, die young? Investigating product life spans and obsolescence in an agent-based model

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Abstract

This article presents an agent-based simulation model that explores the dynamics of product life spans. The objective of this modeling exercise is to investigate the interplay between technological change and product life span in extended industrial dynamics. Change in product characteristics is driven by an endogenous stochastic process relying on the interplays between heterogeneous consumers and firms. Special attention is paid to demand-side modeling, which allows analyzing more thoroughly how decisions of bounded rational consumers affect the dynamics of the system. Although most existing models on product life span investigate durable goods monopolists, our study highlights the notion that diversity matters. Diversity of supply and demand in a bounded rationality context can push firms to market products with longer life spans, but their diffusion is restrained by the sensitivity of consumers to product obsolescence and their willingness to pay for longer-lasting products. The dynamics of consumer preferences influenced by firm marketing activities tend to maintain this situation which contributes to locking the market into short product-life trends. Unlocking the system requires changes in consumer behavior and better information about product life span and cost per use for consumers.

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Notes

  1. See, for instance, Noorman and Uiterkamp (1998), Lorek and Spangenberg (2001), and OECD (2002).

  2. Planned obsolescence may be found in complex products such as printers that stop functioning after a programmed number of copies, batteries for cellphones that have much shorter life spans than the other components but that are difficult or impossible to take apart and to replace, and TV sets made with particular undersized electronic components. It may also include simple products such as razor blades or nylon stockings that have to be changed very frequently even though it has been proved possible to make them more durable by using alternative materials. For a more detailed discussion on this question, see Slade (2006).

  3. For an extended review on these models, see Waldman (2003).

  4. For a more detailed discussion on this question, see Valente (2012).

  5. Consequently, i represents the product as well as the firm.

  6. We do not model economies of scale or economies of scope that reduce production costs and then product price.

  7. An overview of the model parameters is presented in Table 5 in Appendix.

  8. Consumers owning a product with the highest performance will have an obsolescence probability equal to zero; those owning a product with the lowest performance will have the maximum probability obso.

  9. Note that the depreciation rate of the monetary value of products would affect the renewal decision of consumers. The rate at which products depreciate in value over time would influence the point at which owners consider them no longer worth maintaining and will affect their second-hand value (Cooper 2010a). Because we focus on technological obsolescence and we do not model any second-hand markets, we do not take into account this depreciation rate and assume that consumers consider only the technical quality to decide whether to discard a functional product or not.

  10. For a more detailed discussion on product recyclability and life span, see Brouillat (2009).

  11. To not needlessly complicate the model, we do not model any production or cost functions for firms, and consequently we do not calculate any profit.

  12. Valente (2012) provides a detailed discussion on preference origins and the impact of marketing activities.

  13. We assume perfect imitation of product characteristics for simplification reasons. In fact, one can reasonably think that imitation is not perfect in the real world and firms need time to market the same products as those of the copied firms. However, modeling such a learning process would unnecessarily complicate the current model.

  14. These costs are a given part of a firm’s turnover, identical for all the firms. They will then lower the firm’s turnover during the transition period.

  15. We assume that imitation and innovation can occur in the same period: firms can imitate the product technical quality of a competitor and then improve this quality through innovation.

  16. It can also be interpreted as the share of turnover invested in innovation activities.

  17. α is initially the same for all the firms.

  18. We assume that every firm is able to market products with short or long life spans. The upper limit of the admissible range for L reflects the maximum feasible technical life span given the physical properties of materials and components that may constitute products. This limit is assumed to be fixed.

  19. The choice between imitation and exploration is random, based on a fixed probability.

  20. Laboratory for Simulation Development is a simulation platform developed by Valente (2008). It is downloadable at the following address: www.labsimdev.org.

  21. maxX is the best product technical quality on the market. maxX can become superior to 1 (the initial maximum value for X) thanks to innovation.

  22. If consumers do not buy any products over the period because they own a still-functional product, the surplus for this period is maximal, equal to its reserve price. If consumers do not buy any products during the period because of excessive prices compared to reserve prices, the surplus for this period is null.

  23. A box plot gives the quartiles of the distribution of the considered variable as well as its maximal and minimal values. The dot represents the mean of the variable.

  24. Student T-tests show significant differences between series.

  25. Student T-tests show significant differences between series.

  26. There is a dominant design, that is, only products with long life spans or only products with short life spans, in 42.88 % of the 10,000 scenarios. There are only Hightech-Long products on the market in 1.50 % of the 10,000 scenarios, only Lowtech-Long products in 4.20 %, only Hightech-Short products in 6.17 %, and only Lowtech-Short products in 12.86 %.

  27. The average market share of long life span products (based on the number of users of these products) over the 10,000 scenarios is 30.97 %. This market share is less than 10 % in 52 % of the 10,000 scenarios.

  28. Source: BBC News Business, September 7th, 2012. http://www.bbc.co.uk/news/business-19515485

  29. Dependent variables are the share of each firm category (Hightech-Long, Hightech-Short, Lowtech-Long, Lowtech-Short) in the total population of firms, the average position of product characteristics in consumer ranking, the total amount of waste, the average technical quality of products, and the consumer surplus. Independent variables are β 1 , β 2 , λ, obso, m and high-ResP.

  30. If we consider, for instance, the Lowtech-Short tree in Fig. 5, on the left branch, we have all observations for which high-resP ≥ 0.679. On the right branch, we have all observations for which high-resP < 0.679. When high-resP < 0.679 and m < 0.2937, the expected value for the share of firms on the market selecting the Lowtech-Short strategy is 38.25 %, and we have n = 2042 observations corresponding to this case.

  31. The higher the value for high-resP, the higher the reserve prices.

  32. At least 38.25 % of the firms would choose this strategy when reserve prices are low, although it falls to less than 26 % when they are higher.

  33. Reminder: the higher m, the less tolerant consumers are regarding dominated options at the first stages of the TTB algorithm.

  34. One may notice that high tolerance of consumers would also decrease the attractiveness for the Lowtech-Short strategy when reserve prices are low.

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  1. GREThA (UMR CNRS 5113) – University of Bordeaux (France), Avenue Léon Duguit, 33608, Pessac, France

    Eric Brouillat

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Correspondence to Eric Brouillat.

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Table 5 Overview of parameters
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Brouillat, E. Live fast, die young? Investigating product life spans and obsolescence in an agent-based model. J Evol Econ 25, 447–473 (2015). https://doi.org/10.1007/s00191-014-0385-1

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