Trade runner 2049: Complexity, development, and the future of the economy
Satellite Meeting of the Conference on Complex Systems (CCS) 2018 in Thessaloniki, Sep. 23-28, 2018
|Torsten Heinrich||Claudius Gräbner|
|Institute for New Economic Thinking (INET)||Institute for Comprehensive Analysis of the Economy (ICAE)|
|Oxford Martin School, University of Oxford, Oxford, UK||Johannes Kepler University Linz, Linz, Austria|
Building on successful satellite meetings in the previous two years in the context of the Conference on Complex Systems, we propose to continue holding a satellite meeting on the future of economic systems. The prime focus will be - as usual - on the opportunities and challenges arising from complex systems science in economics. Another focus of the conference satellite will be on economic development and growing inequality. This year's conference location provides for an excellent environment to discuss these challenges.
Recent developments in fields such as network science, artificial intelligence and evolutionary dynamics have revolutionized the modern approach to complex adaptive systems (Mitchell, 2009). It became possible to work with systems of massive complexity. The limits of computation power and data storage capacity as well as those in the minds of the researchers were pushed. Methods, approaches, and objectives that had never seriously been considered before now became feasible.
Yet, in economics, the application of these is just at the beginning. A complexity-based approach to economics, however, is as scientifically promising as it is urgently needed: Agent-based models allow to work with truly microfounded models with massive numbers of agents, simulating entire sectors, or even entire economies (Foley and Farmer, 2009, Caiani et al., 2016, Tesfatsion, 2017). Advances in machine learning and the improved situation of available data allows the efficient estimation and calibration of those models (Fagiolo et al., 2017, Barde and van der Hoog, 2017, Salle and Yıldızoğlu, 2014, Lamperti et al., 2017).
The satellite places a second focus on economic development and growing inequality. While we may hope that above mentioned methods can contribute to resolving pressing issues like poverty, growing inequality, or environmental challenges arising from economic systems, we still have a long way to go. In fact, it is not clear, if the opportunities presented by new methods and technologies will be part of the solution to these questions or if they may also come to constitute part of the problem.
The economic and social implications of new technologies are not as well-understood as it seemed only recently. Consider for example the effects of big data on recent elections combined with the failure of established pre-election polls to provide accurate predictions, or the effects that big data may have on privacy and, in some countries, on freedom, political control, and economic opportunity. Other examples include the economic implications of autonomous vehicles, AI understanding of natural languages, industry 4.0, progressive social centrality of digital social networks, the "death of privacy", and the wide availability of big data in virtually every economic sector.
While all contributions related to the broad topics are welcome, the satellite meeting will invite submissions on three topics in particular:
- Challenges of growing inequality. In many countries, inequality does again seem to be on the rise. Notwithstanding gigantic advances in technology and fabulous productivity increases in recent decades, many regions continue to suffer from poverty, unemployment and lack of opportunity. Greece, this year's host country of the conference may serve as one example. What solutions does complex systems science have to offer (Hausmann, 2013, Hartmann, 2017, Beretta et al., 2018)? How can new methods be employed to tackle the challenges ahead?
- Trade networks and economic development. One of the most promising fields of complexity economics is the research in the context of the product space and its implications for economic development, trade, and the future of industrial organization (Hidalgo, 2009, Hausmann, 2013, Tacchella, 2013). Yet, it is still an open question to what extent complexity economics may help us to understand the mechanisms underlying knowledge accumulation, diffusion, and the implications for development processes? What policy recommendations can we offer based on the complexity approach to economic development?
- Insights from complex systems science and machine learning in data from economics. Economics is a field that offers huge and detailed data sources. Be it financial market data or data on patents or industrial organization, many data sets are waiting to be explored with the ever increasing range of methods of modern data analytics. Which insights can complex systems science provide in this field? How can predictive tools used to develop theories of the underlying economic mechanisms? What role does the reflexive nature of economic systems play for the application of such tools?
These and many other topics have the potential to become pathbreaking for economics and central for applied complex systems science. While both fields - economics and complex systems science - are characterized by a cautious mutual interest, the connections between the two research camps are still negligible.
This is unfortunate since the continued interdisciplinary exchange between complexity science on the one hand and institutional and evolutionary economics on the other is important for both sides, and bears the potential of leading to yield both fundamental and highly policy-relevant insights. Therefore, this satellite aims at bringing together exceptional scholars from both research communities to push forward the complexity approach to economics.
[Barde and van der Hoog, 2017] Barde, S. and van der Hoog, S. (2017). An empirical validation protocol for large-scale agent-based models. University of Kent School of Economics Discussion Papers: KPDE 1712.
[Beretta et al., 2018] Beretta, E., Fontana, M., Guerzoni, M., and Jordan, A. (2018). Cultural dissimilarity: Boon or bane for technology diffusion? Technological Forecasting and Social Change, pages -. forthcoming.
[Caiani et al., 2016] Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., and Stiglitz, J. E. (2016). Agent based-stock flow consistent macroeconomics: Towards a benchmark model. Journal of Economic Dynamics and Control, 69:375-408.
[Fagiolo et al., 2017] Fagiolo, G., Guerini, M., Lamperti, F., Moneta, A., Roventini, A., et al. (2017). Validation of agent-based models in economics and finance. LEM Papers Series, 23.
[Foley and Farmer, 2009] Foley, D. K. and Farmer, J. D. (2009). The economy needs agent-based modelling. Nature, 460(6):685-686.
[Hartmann et al., 2017] Hartmann, D., Guevara, M. R., Jara-Figueroa, C., Aristarán, M., and Hidalgo, C. A. (2017). Linking Economic Complexity, Institutions, and Income Inequality. World Development, 93:75-93.
[Hausmann et al., 2013] Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., and Yildirim, M. A. (2013). The Atlas of Economic Complexity. Mapping Paths to Prosperity. The MIT Press, Cambridge, MA, and London, UK.
[Hidalgo and Hausmann, 2009] Hidalgo, C. A. and Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26):10570-10575.
[Lamperti et al., 2017] Lamperti, F., Roventini, A., and Sani, A. (2017). Agent-based model calibration using machine learning surrogates.
[Mitchell, 2009] Mitchell, M. (2009). Complexity. A Guided Tour. Oxford University Press, Oxford and New York.
[Salle and Yıldızoğlu, 2014] Salle, I. and Yıldızoğlu, M. (2014). Efficient sampling and meta-modeling for computational economic models. Computational Economics, 44(4):507-536.
[Tacchella et al., 2013] Tacchella, A., Cristelli, M., Caldarelli, G., Gabrielli, A., and Pietronero, L. (2013). Economic complexity: Conceptual grounding of a new metrics for global competitiveness. Journal of Economic Dynamics and Control, 37(8):1683-1691.
[Tesfatsion, 2017] Tesfatsion, L. (2017). Modeling Economic Systems as Locally-Constructive Sequential Games. Journal of Economic Methodology.
How to Submit
||Notification of Acceptance: Jun 28, 2018|
Please submit your abstract of 300-750 words to our submission website on easychair.org. All speakers must register for the Conference on Complex Systems 2018 in Thessaloniki
Notification of acceptance until Jun 28 allows for early registration for the CCS conference (until Jun 30, 2018) after acceptance.