The complexity of the intangible digital economy: an agent-based model

Bertani, Filippo and Ponta, Linda and Raberto, Marco and Teglio, Andrea and Cincotti, Silvano (2019): The complexity of the intangible digital economy: an agent-based model.

Abstract

During the last decades, we have witnessed a strong development of intangible digital technologies. Software, artificial intelligence and algorithms are increasingly affecting both production systems and our lives; economists have started to figure out the long-run complex economic implications of this new technological wave. In this paper, we address this question through the agent-based modelling approach. In particular, we enrich the macroeconomic model Eurace with the concept of intangible digital technology and investigate its effects both at the micro and macro level. Results show the emergence of the relevant stylized facts observed in the business domain, such as increasing returns, winner-take-most phenomena and market lock-in. At the macro level, our main finding is an increasing unemployment level, since the sizeable decrease of the employment rate in the mass-production system, provided by the higher productivity of digital assets, is usually not counterbalanced by the new jobs created in the digital sector.

• Item Type: MPRA Paper
• Language: English
• Keywords: Intangible assets, Digital transformation, Technological unemployment, Agent-based economics
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes

References

Acemoglu, D., Restrepo, P., 2017. Robots and Jobs: Evidence from US Labor Markets. Working Paper 23285. National Bureau of Economic Research.

Acemoglu, D., Restrepo, P., 2018a. Artificial Intelligence, Automation and Work. Working Paper 24196. National Bureau of Economic Research.

Acemoglu, D., Restrepo, P., 2018b. Automation and new tasks: The implications of the task content of production for labor demand. Journal of Economic Perspectives 33, 3-30.

Acemoglu, D., Restrepo, P., 2018c. Low-skill and high-skill automation. Journal of Human Capital 12, 204-232.

Acemoglu, D., Restrepo, P., 2018d. Modeling automation. AEA Papers and Proceedings 108, 48-53.

Aghion, P., Jones, B.F., Jones, C.I., 2017. Artificial Intelligence and Economic Growth. Working Paper 23928. National Bureau of Economic Research.

Arthur, W.B., 1989. Competing technologies, increasing returns, and lock-in by historical events. Economic Journal 99, 116-31.

Arthur, W.B., 1990. Positive feedbacks in the economy. Scientific American 262, 92-99.

Arthur, W.B., 1994. Increasing Returns and Path Dependence in the Economy. University of Michigan Press.

Arthur, W.B., 1996. Increasing returns and the new world of business. Harvard business review 74, 100-109.

Arthur, W.B., 1999. Complexity and the economy. Science 284, 107-109.

Arthur, W.B., 2014. Complexity and the Economy. Oxford University Press.

Belleflamme, P., Peitz, M., 2018. Platforms and network effects, in: Handbook of Game Theory and Industrial Organization: Applications. volume 2, pp. 286-317.

Brynjolfsson, E., McAfee, A., 2011. Race Against The Machine: How The Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and The Economy. Digital Frontier Press.

Brynjolfsson, E., McAfee, A., 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Co Inc.

Caiani, A., Russo, A., Gallegati, M., 2019. Does inequality hamper innovation and growth? An AB-SFC analysis. Journal of Evolutionary Economics 29, 177-228.

Carroll, C.D., 2001. A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives 15, 23-45.

Cotteleer, M.M., Sniderman, B., 2017. Forces of change: Industry 4.0. Deloitte Insights.

Dawid, H., Gemkow, S., 2014. How do social networks contribute to wage inequality? Insights from an agent-based analysis. Industrial and Corporate Change 23, 1171-1200.

Dawid, H., Gemkow, S., Harting, P., Kabus, K., Wersching, K., Neugart, M., 2008. Skills, innovation, and growth: An agent-based policy analysis. Journal of Economics and Statistics 228, 251-275.

Dawid, H., Harting, P., van der Hoog, S., Neugart, M., 2019. Macroeconomics with heterogeneous agent models: fostering transparency, reproducibility and replication. Journal of Evolutionary Economics 29, 467-538.

Dawid, H., Harting, P., Neugart, M., 2014. Economic convergence: Policy implications from a heterogeneous agent model. Journal of Economic Dynamics and Control 44, 54-80.

Dawid, H., Harting, P., Neugart, M., 2018. Cohesion policy and inequality dynamics: Insights from a heterogeneous agents macroeconomic model. Journal of Economic Behavior and Organization 150, 220-255.

Dawid, H., Reimann, M., 2011. Diversification: a road to inefficiency in product innovations? Journal of Evolutionary Economics 21, 191-229.

Doraszelski, U., Jaumandreu, J., 2018. Measuring the Bias of Technological Change. Journal of Political Economy 126, 1027-108.

Dosi, G., Fagiolo, G., Roventini, A., 2010. Schumpeter meeting keynes: A policy-friendly model of endoge- nous growth and business cycles. Journal of Economic Dynamics and Control 34, 1748-1767.

Elsner, W., Heinrich, T., Schwardt, H., 2014. The Microeconomics of Complex Economies. 1 ed., Elsevier.

Fabiani, S., Druant, M., Hernando, I., Kwapil, C., Landau, B., Loupias, C., Martins, F., Math, T., Sabbatini, R., Stahl, H., Stokman, A., 2006. What Firms' Surveys Tell Us about Price-Setting Behavior in the Euro Area. International Journal of Central Banking 2, 3-47.

Fanti, L., 2018. An AB-SFC Model of Induced Technical Change along Classical and Keynesian Lines. MPRA Paper. University Library of Munich, Germany.

Farrell, J., Klemperer, P., 2007. Chapter 31: Coordination and Lock-In: Competition with Switching Costs and Network Effects, in: Handbook of Industrial Organization. volume 3, pp. 1967-2072.

Fontana, M., 2010. Can neoclassical economics handle complexity? the fallacy of the oil spot dynamic. Journal of Economic Behavior & Organization. 76, 584-596.

Fraser, R.W., 1985. Uncertainty and the theory of mark-up pricing. Bulletin of Economic Research 37, 55-64.

Gallegati, M., 2018. Complex agent-based models. New Economic Windows, Springer.

Godin, A., Caverzasi, E., 2014. Post-Keynesian stock- ow-consistent modelling: a survey. Cambridge Journal of Economics 39, 157-187.

Godley, W., Lavoie, M., 2012. Monetary economics: An integrated approach to credit, money, income, production and wealth. Palgrave Macmillan UK.

Goldstein, J., 1986a. Markup Variability and Flexibility: Theory and Empirical Evidence. The Journal of Business 59, 599-621.

Goldstein, J.P., 1986b. Mark-Up Pricing over the Business Cycle: The Microfoundations of the Variable Mark-Up. Southern Economic Journal 53, 233.

Good, I.J., 1966. Speculations concerning the first ultraintelligent machine. Advances in Computers 6, 31-88.

Goos, M., Manning, A., 2007. Lousy and lovely jobs: The rising polarization of work in britain. Review of Economics and Statistics 89, 118-133.

Haskel, J., Westlake, S., 2017. Capitalism without Capital. Princeton University Press.

Heinrich, T., 2018. Network Externalities and Compatibility Among Standards: A Replicator Dynamics and Simulation Analysis. Computational Economics 52, 809-837.

Hommes, C., LeBaron, B., 2018. Computational Economics: Heterogeneous Agent Modeling. North Holland.

Kalt, J.P., 1978. Technological Change and Factor Substitution in the United States: 1929- 1967. Interna- tional Economic Review 19, 761-775.

Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., Noh, S.D., 2016. Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology 3, 111-128.

Kwoka, J., 1985. The herfindahl index in theory and practice. Antitrust Bulletin 30, 915-947.

Mazzocchetti, A., Raberto, M., Teglio, A., Cincotti, S., 2018. Securitization and business cycle: an agent- based perspective. Industrial and Corporate Change 27, 1091-1121.

Mokyr, J., Vickers, C., Ziebarth, N.L., 2015. The history of technological anxiety and the future of economic growth: Is this time different? Journal of Economic Perspectives 29, 31-50.

Nordhaus, W.D., 2015. Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth. SSRN Electronic Journal.

North, M., Macal, C., 2007. Managing Business Complexity: Discovering Strategic Solutions With Agent-Based Modeling and Simulation. Oxford University Press.

Ozel, B., Nathanael, R.C., Raberto, M., Teglio, A., Cincotti, S., 2019. Macroeconomic implications of mortgage loan requirements: an agent-based approach. Journal of Economic Interaction and Coordination 14, 7-46.

Parrott, A., Lane, W., 2017. Industry 4.0 and the power of the digital twin. Deloitte University Press.

Petrovic, M., Ozel, B., Teglio, A., Raberto, M., Cincotti, S., 2017. Eurace Open: An agent-based multi- country model. Working Paper 2017/09. Economics Department, Universitat Jaume I, Castellon (Spain).

Platt, D., 2019. A Comparison of Economic Agent-Based Model Calibration Methods. SSRN Electronic Journal.

Plott, C.R., Sunder, S., 1982. Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models. Journal of Political Economy 90, 663-698.

Ponta, L., Raberto, M., Teglio, A., Cincotti, S., 2018. An Agent-based Stock-fow Consistent Model of the Sustainable Transition in the Energy Sector. Ecological Economics 145, 274-300.

Pyka, A., Gilbert, N., Ahrweiler, P., 2010. Agent-based modelling of innovation networks - the fairytale of spillover. Understanding Complex Systems 2009, 101-126.

Raberto, M., Ozel, B., Ponta, L., Teglio, A., Cincotti, S., 2018. From financial instability to green finance: the role of banking and credit market regulation in the eurace model. Journal of Evolutionary Economics, 1-37.

Raberto, M., Teglio, A., Cincotti, S., 2012. Debt, deleveraging and business cycles: An agent-based perspec- tive. Economics - The Open-Access, Open-Assessment E-Journal 6, 1-49.

Solow, R., 1957. Technical Change and the Aggregate Production Function. The Review of Economics and Statistics 39, 312-320.

Teglio, A., Mazzocchetti, A., Ponta, L., Raberto, M., Cincotti, S., 2019. Budgetary rigour with stimulus in lean times: Policy advices from an agent-based model. Journal of Economic Behavior and Organization 157, 59-83.

Teglio, A., Raberto, M., Cincotti, S., 2012. The impact of banks' capital adequacy regulation on the economic system: An agent-based approach. Advances in Complex Systems 15, 1-27.

Uzawa, H., 1961. Neutral Inventions and the Stability of Growth Equilibrium. The Review of Economic Studies 28, 117-124.

Vermeulen, B., Kesselhut, J., Pyka, A., Saviotti, P.P., 2018. The impact of automation on employment: Just the usual structural change? Sustainability 10, 1-27.

Vermeulen, B., Pyka, A., 2014. Technological progress and effects of (Supra) regional innovation and production collaboration. An agent-based model simulation study, in: IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr), pp. 357-364.

Vermeulen, B., Pyka, A., 2018. The Role of Network Topology and the Spatial Distribution and Structure of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based Model Study. Computational Economics 52, 773-808.

Vivarelli, M., 2014. Innovation, employment and skills in advanced and developing countries: A survey of economic literature. Journal of Economic Issues 48, 123-154.