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Against the backdrop of innovation fault-tolerance, government compensation for failed generic technological innovation projects is beneficial to stimulate re-innovation behaviour. However, considering the information asymmetry, a collusion tendency exists between the compensated party and the evaluator during the process of compensation. To prompt the government to build collusion-proof mechanisms to reduce collusion loss, the evolutionary game method was used to build replicated dynamic equations and a Jacobian matrix of both sides based on the information topology between the conspirators and regulator. Through the evolutionary equilibrium analysis and numerical simulation, evolutionary stability strategies (ESS) under different topological relationship information (non-intersect, partial intersect, and inclusive type) were found. Results show that the collusion behaviour can be effectively restrained when the government is unaware of collusion information, the net defense income is positive, and the penalty threshold is the product of the net collusive income and the ratio of the collusive and regulatory information. With the increasing amount of collusive information available to the government, the conspirators tend to adhere to moral principles subject to strict regulation. In addition, the moderating effect of penalty factor is positively correlated with the private information possessed by both sides. The conclusion is beneficial to provide theoretical support for optimizing the government-led compensation mechanism for failed generic technological innovation projects.

To quantify the difference of distinct stochastic processes it is not sufficient to consider the distance of their states and corresponding probabilities. Instead, the information, which evolves and accumulates over time and which is mathematically encoded by filtrations, has to be accounted for as well. The nested distance , also known as bicausal Wasserstein distance , recognizes this component and involves the filtration properly. This distance is of emerging importance due to its applications in stochastic analysis, stochastic programming, mathematical economics and other disciplines. This paper investigates the basic metric and topological properties of the nested distance on the space of discrete-time processes. In particular we prove that the nested distance generates a Polish topology, although the genuine space is not complete. Moreover we identify its completion to be the space of nested distributions , a space of generalized stochastic processes. [ABSTRACT FROM AUTHOR]

Abstract: In its modern formulation, the Maximum Entropy Principle was promoted by E.T. Jaynes, starting in the mid-fifties. The principle dictates that one should look for a distribution, consistent with available information, which maximizes the entropy. However, this principle focuses only on distributions and it appears advantageous to bring information theoretical thinking more prominently into play by also focusing on the "observer" and on coding. This view was brought forward by the second named author in the late seventies and is the view we will follow-up on here. It leads to the consideration of a certain game, the Code Length Game and, via standard game theoretical thinking, to a principle of Game Theoretical Equilibrium. This principle is more basic than the Maximum Entropy Principle in the sense that the search for one type of optimal strategies in the Code Length Game translates directly into the search for distributions with maximum entropy. In the present paper we offer a self-contained and comprehensive treatment of fundamentals of both principles mentioned, based on a study of the Code Length Game. Though new concepts and results are presented, the reading should be instructional and accessible to a rather wide audience, at least if certain mathematical details are left aside at a rst reading. The most frequently studied instance of entropy maximization pertains to the Mean Energy Model which involves a moment constraint related to a given function, here taken to represent "energy". This type of application is very well known from the literature with hundreds of applications pertaining to several different elds and will also here serve as important illustration of the theory. But our approach reaches further, especially regarding the study of continuity properties of the entropy function, and this leads to new results which allow a discussion of models with so-called entropy loss. These results have tempted us to speculate over the development of natural languages. In fact, we are able to relate our theoretical findings to the empirically found Zipf's law which involves statistical aspects of words in a language. The apparent irregularity inherent in models with entropy loss turns out to imply desirable stability properties of languages.

In this study, the authors address the leaderless consensus problem of multiple robotic manipulators with communication delays. Taking dynamic uncertainties of the robotic agents into consideration, they perform the distributed adaptive position feedback control scheme design for strongly connected information topology, where the communication is subject to constant and bounded time delays. The novel distributed sliding observer is first proposed such that the joint velocities can be estimated online. Then, the distributed adaptive controller with dynamic parameter updating is constructed based on the observed information. The convergence of consensus errors is proved with Lyapunov stability analysis tool. Finally, simulations are performed on networked robotic manipulators to validate the effectiveness of the theoretical approach. [ABSTRACT FROM AUTHOR]