Abstract
In this chapter, we have explored some philosophical designs for developing economics around systemic risks viewed in terms of their internal probability. This concept of probability is based on observations of living systems. An important point to note is that cognizing subjects are not limited to humans but also extend to materials. The material observer behaves as an “internal observer.” Further, material interactions between the observer and the objects being observed must generate a complex process. Based on this conception, we aimed to develop an understanding of how systemic risks could be recognized in this kind of interaction. In line with this objective, we examined this in relation to immunology and immunoediting that occurs on the surface level of the immune system. Our exploration revealed the importance of checkpoints such as nodes within a networked structure that is formed based on the above complex interactions, enabling us to better understand systemic risks. Last, we focused on the network node or link of the input-output system of the economic system, and introduced network-related concepts such as alpha centrality, successfully identifying a measure of risk in Japan’s current economic system.
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- 1.
Nakajima (2013:72) has noted: “Unlike the demon, it is useful to define the external observer as an entity that includes both omniscient and non-omniscient observers. In this case, if the external observer is omniscient, measurements obtained by observation can correspond to system states in a one-to-one manner, irrespective of whether the observed system is deterministic.”
- 2.
This part is suggested by Prof. Hiroshi Yoshikawa’s final lecture at Faculty of Economics, University of Tokyo, March 5, 2016. In addition, another lineage from thermodynamics to apply a blood circuit, as Mimkes (2017) suggested:
- 3.
- 4.
The immune system has two kinds of checkpoints. One variety of checkpoints is found on the surface level of the immune system (see Pardoll 2016). The other type is associated with the cell division cycle which coordinates the reproduction of cells. For details on the latter, see Bartek and Lukas (2007) and Syljuaasen (2007).
- 5.
See Xie et al. (2016).
- 6.
National Cancer Institute (2016).
- 7.
Data provided by Director-General for Policy Planning (Statistical Standards 2016) were applied.
- 8.
- 9.
See Nikaido (1968:90–92) for details.
- 10.
These data have been kindly provided by Prof. Hiroshi Iyetomi.
- 11.
See Bonacich and Paulette (2001) for a discussion on α centrality. Eigenvector centrality may be used to characterize the survival pathway based on the rate of profit/growth. The latter seems to be related to the idea of a cell death cycle (CDC).
- 12.
Ultimately, the valuation by producers’ prices underlies the international equilibrium price system allowing multiple intermediate products to be traded. This kind of equilibrium remained unproven over a period of two centuries until Shiozawa (2015, 2007) presented an elegant proof using sub-tropical geometry.
- 13.
- 14.
Tran et al. (2016) have attempted to apply α centrality to the classical matrix without explicitly considering the import factor. However, this article focuses on an international comparison of the indices driven by the centrality among the advanced countries using global input-output data.
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Aruka, Y. (2017). Systemic Risks in the Evolution of Complex Social Systems. In: Aruka, Y., Kirman, A. (eds) Economic Foundations for Social Complexity Science. Evolutionary Economics and Social Complexity Science, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-5705-2_2
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