Is It Possible to Visualise Any Stock Flow Consistent Model as a Directed Acyclic Graph?

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

Download Full-text

Risk identification and modeling for blockchain-enabled container shipping

International Journal of Physical Distribution & Logistics Management ◽

10.1108/ijpdlm-01-2020-0036 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Son Nguyen ◽

Peggy Shu-Ling Chen ◽

Yuquan Du

Keyword(s):

Design Methodology ◽

Directed Acyclic Graph ◽

Risk Identification ◽

Container Shipping ◽

Content Type ◽

Acyclic Graph ◽

Operational Risks ◽

Systems Security ◽

Event Risk ◽

Potential Risks

PurposeAlthough being considered for adoption by stakeholders in container shipping, application of blockchain is hindered by different factors. This paper investigates the potential operational risks of blockchain-integrated container shipping systems as one of such barriers.Design/methodology/approachLiterature review is employed as the method of risk identification. Scientific articles, special institutional reports and publications of blockchain solution providers were included in an inclusive qualitative analysis. A directed acyclic graph (DAG) was constructed and analyzed based on network topological metrics.FindingsTwenty-eight potential risks and 47 connections were identified in three groups of initiative, transitional and sequel. The DAG analysis results reflect a relatively well-connected network of identified hazardous events (HEs), suggesting the pervasiveness of information risks and various multiple-event risk scenarios. The criticality of the connected systems' security and information accuracy are also indicated.Originality/valueThis paper indicates the changes of container shipping operational risk in the process of blockchain integration by using updated data. It creates awareness of the emerging risks, provides their insights and establishes the basis for further research.

Download Full-text

Heterogeneous expectations, forecasting behaviour and policy experiments in a hybrid Agent-based Stock-flow-consistent model

Journal of Evolutionary Economics ◽

10.1007/s00191-020-00683-7 ◽

2020 ◽

Author(s):

Severin Reissl

Keyword(s):

Heterogeneous Expectations ◽

Agent Based ◽

Consistent Model ◽

Policy Experiments ◽

Hybrid Agent ◽

Stock Flow

Download Full-text

Household debt and macroeconomic stability: An empirical stock‐flow consistent model for the Danish economy

Metroeconomica ◽

10.1111/meca.12358 ◽

2021 ◽

Author(s):

Mikael Randrup Byrialsen ◽

Hamid Raza

Keyword(s):

Household Debt ◽

Consistent Model ◽

Stock Flow ◽

Macroeconomic Stability

Download Full-text

Money creation under full-reserve banking: a stock–flow consistent model

Cambridge Journal of Economics ◽

10.1093/cje/bey034 ◽

2018 ◽

Vol 43 (5) ◽

pp. 1219-1249 ◽

Cited By ~ 2

Author(s):

Patrizio Lainà

Keyword(s):

Interest Rates ◽

Government Spending ◽

Banking System ◽

Large Change ◽

Real Economy ◽

Consistent Model ◽

Safety Margins ◽

Money Creation ◽

Reserve Banking ◽

Stock Flow

Abstract This paper presents a stock–flow consistent model of full-reserve banking. The paper investigates money creation through government spending in a full-reserve banking system. The results are contrasted against the cases in which government spending is increased under full-reserve banking without money creation and under endogenous money, that is, the current monetary system. It is found that output, employment and inflation evolve almost identically. In contrast to other cases, money creation in a full-reserve banking system leads to a permanent reduction in consolidated government debt. Monetary policy transmits effectively as an increase in central bank reserves translates into an almost equal increase in demand deposits. Furthermore, an unusually large change in the money supply induces only smooth and relatively small changes in interest rates. In addition, the paper compares three additional ways to create money. Money creation through tax cuts or citizen’s dividend generates roughly the same results as creating money through government spending. In contrast, money creation through quantitative easing affects only monetary aggregates and interest rates but not the real economy. Although in every money creation experiment banks are able to fully satisfy the demand for loans, temporary credit crunches can occur under full-reserve banking. The occurrence of credit crunches depends on changes in private behaviour and economic policy as well as safety margins adopted by banks.

Download Full-text