Next Article in Journal
Encouraging Entrepreneurship and Economic Growth
Next Article in Special Issue
The Impacts of Selling Expense Structure on Enterprise Growth in Large Enterprises: A Study from Vietnam
Previous Article in Journal
Foreign Direct Investment and Economic Growth in the Short Run and Long Run: Empirical Evidence from Developing Countries
Previous Article in Special Issue
Global Asset Allocation Strategy Using a Hidden Markov Model
Open AccessConcept Paper

Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud

by Qi Deng 1,2,3
1
Accounting and Finance Group, International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2
Cofintelligence Financial Technology Ltd., Hong Kong, China
3
Cofintelligence Financial Technology Ltd., Shanghai 201106, China
J. Risk Financial Manag. 2019, 12(4), 177; https://doi.org/10.3390/jrfm12040177
Received: 12 October 2019 / Revised: 18 November 2019 / Accepted: 21 November 2019 / Published: 25 November 2019
(This article belongs to the Special Issue AI and Financial Markets)
The Artificial Intelligence BlockCloud (AIBC) is an artificial intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer (the latter three are the collective “upper-layers”). The AIBC layers have distinguished responsibilities and thus performance and robustness requirements. The upper layers need to follow a set of economic policies strictly and run on a deterministic and robust protocol. While the fundamental layer needs to follow a protocol with high throughput without sacrificing robustness. As such, the AIBC implements a two-consensus scheme to enforce economic policies and achieve performance and robustness: Delegated Proof of Economic Value (DPoEV) incentive consensus on the upper layers, and Delegated Adaptive Byzantine Fault Tolerance (DABFT) distributed consensus on the fundamental layer. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to enforce the DPoEV, as well as to achieve the best balance of performance, robustness, and security. The DPoEV-DABFT dual-consensus architecture, by design, makes the AIBC attack-proof against risks such as double-spending, short-range and 51% attacks; it has a built-in dynamic sharding feature that allows scalability and eliminates the single-shard takeover. Our contribution is four-fold: that we develop a set of innovative economic models governing the monetary, trading and supply-demand policies in the AIBC; that we establish an upper-layer DPoEV incentive consensus algorithm that implements the economic policies; that we provide a fundamental layer DABFT distributed consensus algorithm that executes the DPoEV with adaptability; and that we prove the economic models can be effectively enforced by AIBC’s DPoEV-DABFT dual-consensus architecture. View Full-Text
Keywords: blockchain; BlockCloud; Artificial Intelligence; consensus algorithms blockchain; BlockCloud; Artificial Intelligence; consensus algorithms
Show Figures

Figure 1

MDPI and ACS Style

Deng, Q. Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud. J. Risk Financial Manag. 2019, 12, 177.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop