Trust Asymmetry

In the traditional financial sector, players profited from information asymmetries. In the blockchain financial system, they profit from trust asymmetries. Transactions are a flow, trust is a stock. Even if the information asymmetries across the medium of exchange are close to zero (as it is expected in a decentralized financial system), there exists a “trust imbalance” in the perimeter. This fluid dynamic follows Hayek’s concept of monetary policy: “What we find is rather a continuum in which objects of various degrees of liquidity, or with values which can fluctuate independently of each other, shade into each other in the degree to which they function as money”. Trust-enabling structures are derived using Evolutionary Computing and Topological Data Analysis; trust dynamics are rendered using Fields Finance and the modeling of mass and information flows of Forrester’s System Dynamics methodology. Since the levels of trust are computed from the rates of information flows (attention and transactions), trust asymmetries might be viewed as a particular case of information asymmetries – albeit one in which hidden information can be accessed, of the sort that neither price nor on-chain data can provide. The key discovery is the existence of a “belief consensus” with trust metrics as the possible fundamental source of intrinsic value in digital assets. This research is relevant to policymakers, investors, and businesses operating in the real economy, who are looking to understand the structure and dynamics of digital asset-based financial systems. Its contributions are also applicable to any socio-technical system of value-based attention flows.

Venegas P. (2018) Trust Asymmetry. In: Morales A., Gershenson C., Braha D., Minai A., Bar-Yam Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. Springer Nature Switzerland AG 2018. Google books.

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Genetic Programming Theory and Practice XVII. BANZHAF, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (Eds.) .2020. Springer Nature. DOI 10.1007/978-3-030-39958-0

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Sources: Springer Nature Switzerland AG 2018. Google books.