METHODS AND MODELING OF INFORMATION ASYMMETRY IN THE INNOVATION PROCESS

Анотація

The subject of the innovation and its realization have been substantial in the ongoing
configuration of the economic growth, foremost last 3 centuries. The results of this are showing in
the pace of technological progress and changes in social structures, like arise of working and stratum
from the middle of 19 century.
Conventionally, the main theoretical and practical framework of the realization of innovation
phenomena is innovation process as the specific system of transformation and changes of scientific
ideas to market goods. The scientific community through the evolution of subject discourse has
produced and proposed different variations of this process description. These inquiries had started
from simple demand (push) or supply (pull) driven models and has been continuing to extend to
nonlinear and network-based open innovation theory.
However, comparable reinforcement of theoretical concepts by suitable mathematical apparatus
has been weakly developed nowadays. Especially, there is a leak of the general toolkit of methods to
characterize the innovation process in different circumstances as mathematical objects in the strict
notation. Moreover, it could be stated the absence of at least one comprehensive theory to
mathematical analysis of innovation process, like the neoclassical Solow-Swen growth model for
description of the economic growth.
In our case, the subject of interest is possibility of modelling information asymmetry for the
context of the innovation realization. There is similarity between classical presented cases of
information asymmetry and its conceptual usage for innovation: unknown characteristics and
uncertain result. It could not be predicted the productivity of certain employee, the state of insured
object and the real value of financial asset. Similarly, it could not be defined and known the value of
the result of specific innovation process.
Although in the case of innovation from its nature, its result is information on the specific stages
before its commercialization, as stated in Arrow information paradox (the value for the buyer of
technology/information exists until the sense and details of it is veiled). The one possible consequence
of this is necessity of transaction category inclusion in the description of every model that aim
comprehensive view on the object. For example, this paradox drives the logic of classical patent
systems, which attempt to solve the disclosure problem by trading a temporary monopoly for the
public revelation of knowledge. However, as models of "open innovation" reveal, the reliance on
secrecy versus disclosure remains a continuous strategic tension, often modeled as the paradox of
openness.
From the time of information asymmetry occur situations could be classified as adverse
selection (ex-ante) and moral hazard (ex-post). Therefore, the adverse selection situation describes
the form of market relationships, in contrast to moral hazard which is the case of principal-agent
problem. This could stipulate the specific methodological prerequisites on the modelling framework.
For innovation field, adverse selection is proceeding when financing of novations take place.
The acquisition and learning of information hidden in the market could be done by typical
instruments like screening and signaling. In the case of the innovation screening is constituted by the
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formal procedures like external. However, when direct verification (screening) is too costly or
impossible, the market relies on signaling games. Research identifies several specific actions that
function as valid economic signals in the innovation process. While patents provide legal protection,
their secondary economic function is signaling. A patent application reveals technical details and
certifies "novelty" as value too. Also, an entrepreneur investing their own wealth or forgoing a market
salary is a powerful signal, costly to mimic. Finally, social ties and affiliation could be characterized
as information conduits in. A referral from a trusted network node acts as a certification, which valued
by social capital.