The GED initiative was born from a strong hypothesis of its founders: “Each Economic Ecosystem is a game board, thousands of years of competition have taught us that it is neither near the best way to achieve common or own goals; therefore there are two things in which we need to be fully aware: what role do we have to play in this game, and collaboration is the most important resource we have to play.", from the first premise of the hypothesis, the TE-SER model was born, created by Marcelo Tedesco and Tania Serrano, a model used by GED and other initiatives, as well as researchers worldwide to describe the role that each actor plays in their ecosystem; the second premise is still a work in progress. However, understanding how collaboration works, not only between individuals but between organizations in a society, is a complex task, which has been studied for years and remains an open discussion. In GED we have isolated a series of indicators that we can measure quantitatively and qualitatively in order to identify the patterns of this social dynamic, in order to find points of concordance between each economic ecosystem, and what are the parameters that can best support achieving of better results Each of these parameters contains a wide range of metrics with which we build the indicators.
Roles of Key Organizations in an Economic Ecosystem.
Directionality of first-contact.
Number of attempts to start a collaboration.
Formal vs. Informal collaboration.
Reasons of Collaboration Success and Failures
Allocated Recourses for Collaborations
Methodology and Tools to identify local actors.
We are working to discover which are the most efficient methods to identify actors in economic ecosystems. Although we currently use intensive research techniques in a preliminary phase of the study, in each ecosystem and, subsequently we use the power of Network Social Mapping techniques, unfortunately the resources required for the combination of these methods are usually quite high, and these resources are not always available in all regions of the world; therefore, finding methods and building new tools that could be applied quickly, efficiently and inexpensively is an important step to increase knowledge in the functioning of economic ecosystems in all parts of the world.
Culture and behavior of economic ecosystems.
“No economic ecosystem can be separated from its social ecosystem, they are an integral part of themselves, and both influence each other” Tedesco & Serrano (MIT, 2019).
This complex influence dynamic presents challenges that have never been addressed from the perspective of data science. GED is taking advantage of the data collected in its mapping projects with the objective of constructing reference models that help understand how the behavior of an economic ecosystem is related to the key cultural values (Hofstede, 1999) of the society in which that economic ecosystem is constituted.
We do not (yet) intend to predict the behavior of an economic ecosystem based on mathematical values associated with cultural values and their results in an interactive social network; but we do intend to build knowledge that can be useful for creating positive social engineering strategies based on public policy regarding economic development.
Ecosystem behavior prediction for public policies.
From “snapshots” of the relationship maps of an economic ecosystem taken at two or more times of a timeline and considering the strategies and public policies implemented in the intermediate time, we are learning to predict the outcome of these initiatives in a specific economic ecosystem.
Over time, through causal difference algorithms applied to organizational networks, we can gain the knowledge to determine the influence that a public policy or strategy may have in the future, this knowledge could become invaluable for local and national governments, multilateral institutions and public policy makers, thus, enabling them to evaluate fundamental aspects of the impact of a strategy or public policy before executing it, making the use of public resources more efficient, reducing losses due to uncertainty and, above all, increasing the welfare of citizens.