Complex economic problems and fitness landscapes: Assessment and methodological perspectives

Khraisha, Tamer

doi:10.1016/j.strueco.2019.01.002

Cited by 9 publications

(5 citation statements)

References 98 publications

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“…The FSMC integrates dynamic aspects in the modeling obtained from empirical data, which are two requisites to construct more realistic empirical fitness landscapes (Khraisha 2020). Indeed, the notion of fitness landscape is central to the subfield of complexity economics (Arthur 2013), i.e.…”

Section: Discussionmentioning

confidence: 99%

Fluctuating selection at work: Estimating the firms’ fitnesses from time series of their market caps and forecasting their future shares

Fort

2023

Preprint

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Evolutionary economics has been useful to explain the nature of the processes of innovation, as well as providing useful heuristics for applied research. However, quantitative tests are rare and in general fail to capture the observed dynamics of firms in real markets. A main problem is how to estimate the fitnesses of companies. We present a quantitative implementation of fluctuating selection to explain and forecast the dynamics of firms’ market capitalizations (MC). We start by proposing a recipe to estimate the financial fitness of a company from its MC time series, which is completely analogous to the Malthusian fitness of evolutionary biology. Using Malthusian fitnesses in terms of MC implies that all companies, through their stocks, irrespective of their industry or sector, are competing for investors’ money. These fitnesses, in turn, allow to forecast the companies’ proportions of total MC through a natural selection equation. We validate the method using the daily MC of public-owned Fortune 100 companies across 2000–2021. This Fluctuating Selection from Market Capitalization (FSMC) formula is particularly accurate in forecasting the MC proportions of companies. It outperforms the geometric random walk stochastic benchmark for all forecasting instances across validation periods ranging from a month to a year. JEL codes: B52, C51, C52, C53, C63, G17

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Section: Discussionmentioning

confidence: 99%

Fluctuating selection at work: Estimating the firms’ fitnesses from time series of their market caps and forecasting their future shares

Fort

2023

Preprint

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“…Ma and Nakamori (2005) use a linear mapping obtained by applying a constant matrix to the genome, inspired from the model of Kauffman and Macready (1995). The concept of fitness landscape is applied in different streams of complexity economics (Khraisha, 2020). We choose to work with a similar heuristic, using a complicated fitness landscape obtained from the genome.…”

Section: Agent-based Model Rationalementioning

confidence: 99%

Innovation and informal knowledge exchanges between firms

Raimbault¹

2022

The 2022 Conference on Artificial Life

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Firm clusters are seen as having a positive effect on innovations, what can be interpreted as economies of scale or knowledge spillovers. The processes underlying the success of these clusters remain difficult to isolate. We propose in this paper a stylised agent-based model to test the role of geographical proximity and informal knowledge exchanges between firms on the emergence of innovations. The model is run on synthetic firm clusters. Sensitivity analysis and systematic model exploration unveil a strong impact of interaction distance on innovations, with a qualitative shift when spatial interactions are more intense. Model bi-objective optimisation shows a compromise between innovation and product diversity, suggesting trade-offs for clusters in practice. This model provides thus a first basis to systematically explore the interplay between firm cluster geography and innovation, from an evolutionary perspective.

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“…the number of strategies/actions available for the firm (N); the number of interconnections (interdependencies) between the strategies (K); and the number of co-evolving links between firms/agents (C). These parameters determine the complexity (ruggedness) of the fitness landscape, the relevance of which for organisation studies and economics research has been acknowledged (see Krugman, 1996;Westhoff, Yarbrough, Yarbrough, 1996;Levinthal, 1997;Levinthal and Posen, 2007;Lenox, Rockart, Lewin, 2007;Khraisha, 2019;and Baumann, Schmidt, Stieglitz, 2018). Campos and Fontanari (2019) estimate that for rugged landscapes with K above three, the imitation strategy of another firm's actions is not optimal and agents should explore the landscape independently.…”

Section: Intra-industry Age/size Heterogeneity and Learning On Rugged Fitness Landscapesmentioning

confidence: 99%

Intra-industry firm heterogeneity, sub-optimal adaptation and exit hazard: a fitness landscape approach to firm survival and learning

Trushin

Uğur

2020

Economics of Innovation and New Technology

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We draw on insights from the fitness landscape literature and from models of firm dynamics with learning to hypothesize that: (i) firms in industries with higher company age or size heterogeneity have higher exit hazard after controlling for age, size, and a variety of other predictors of firm survival; and (ii) higher levels of R&D investment mitigate the hazardincreasing effects of industry firm heterogeneity after controlling for the direct effects of R&D intensities at industry and firm level. We test for these novel sources of selection with evidence from a panel dataset of 35,136 R&D-active UK firms from 1998 to 2012 and a range of discrete-time hazard estimators. The findings, which remain robust to multiple sensitivity checks, offer two novel contributions to the literature: (i) firm heterogeneity is not just a passive precondition for subsequent selection process in industry evolution; this heterogeneity enhances selection as more firms might be stranded in suboptimal positions; (ii) firms in more heterogenous industries can mitigate the hazard-increasing effects through R&D investment that facilitates adaptation and search for better fitness locations.

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Complex economic problems and fitness landscapes: Assessment and methodological perspectives

Cited by 9 publications

References 98 publications

Fluctuating selection at work: Estimating the firms’ fitnesses from time series of their market caps and forecasting their future shares

Fluctuating selection at work: Estimating the firms’ fitnesses from time series of their market caps and forecasting their future shares

Innovation and informal knowledge exchanges between firms

Intra-industry firm heterogeneity, sub-optimal adaptation and exit hazard: a fitness landscape approach to firm survival and learning

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