Trust Game and Behaviour Pattern of Public-Private Partnership Project Management Team Based on EEG

Abstract

The Public-Private Partnership (PPP) project is currently one of the main ways for countries to get back from the economic downturn and realize entrepreneurial economy. If the management team wants to achieve long-term stable and vigorous development, it must establish a lasting and reliable trust relationship. The neuroscience research on game has provided new methods and ideas for the study of trust. Based on the game and ERP experiments, this paper constructs a model for the relationships between the factors of trust, individual risk preferences and partner's behaviours and the investment behaviours. At the same time, based on the EEG data with ERP components, this paper further analyzes the internal neural influencing mechanism of how various factors affect the cognitive process and decision-making attitude of trust, modifies, tests and re-modifies the experimental model through enterprise interviews and SEM; and finally obtains the model for the trust recognition mechanism and behaviour pattern among members of the PPP project management team, and proposes measures to keep longterm stable trust between members of the project management team, so as to promote the development and expansion of the PPP project management team

Venture Risk of Small- and Medium-Sized Sci-Tech Enterprises Based on Markov Model

“…On the basis of the above-mentioned research work, this paper determines the positive role of Markov model in the field of risk model of small-and medium-sized high-tech enterprises, constructs a Markov model with a combination of various algorithms, and makes a deep analysis and research on the acquired and collected data by using Markov model, so as to make more effective use of the data, mine the valuable knowledge hidden behind the data, and find out the potential problems that affect the risks of small-and medium-sized high-tech enterprises. [27]. Markov model is used to describe the process of randomly generating an unobservable state random sequence from a hidden Markov chain and then generating an observable random sequence from each state.…”