Supporting Crowd-Powered Science in Economics: FRACTI, a Conceptual Framework for Large-Scale Collaboration and Transparent Investigation in Financial Markets

TL;DR

This paper introduces FRACTI, a conceptual framework designed to facilitate large-scale, transparent, and collaborative scientific investigation in financial markets, emphasizing data sharing, provenance tracking, and multidisciplinary research support.

Contribution

It defines a novel conceptual framework and system architecture for supporting crowd-powered science in economics, enabling large-scale collaboration and transparent data management.

Findings

FRACTI supports scientific method application in computational finance.

The framework enables provenance tracking of data and simulations.

It facilitates large-scale multidisciplinary collaboration in economics.

Abstract

Modern investigation in economics and in other sciences requires the ability to store, share, and replicate results and methods of experiments that are often multidisciplinary and yield a massive amount of data. Given the increasing complexity and growing interaction across diverse bodies of knowledge it is becoming imperative to define a platform to properly support collaborative research and track origin, accuracy and use of data. This paper starts by defining a set of methods leveraging scientific principles and advocating the importance of those methods in multidisciplinary, computer intensive fields like computational finance. The next part of this paper defines a class of systems called scientific support systems, vis-a-vis usages in other research fields such as bioinformatics, physics and engineering. We outline a basic set of fundamental concepts, and list our goals and motivation for leveraging such systems to enable large-scale investigation, "crowd powered science", in economics. The core of this paper provides an outline of FRACTI in five steps. First we present definitions related to scientific support systems intrinsic to finance and describe common characteristics of financial use cases. The second step concentrates on what can be exchanged through the definition of shareable entities called contributions. The third step is the description of a classification system for building blocks of the conceptual framework, called facets. The fourth step introduces the meta-model that will enable provenance tracking and representation of data fragments and simulation. Finally we describe intended cases of use to highlight main strengths of FRACTI: application of the scientific method for investigation in computational finance, large-scale collaboration and simulation.