Computational Economics

Bài báo này trình bày một phương pháp mới để tính toán cân bằng thương lượng Kalai–Smorodinsky cho các trò chơi chuỗi Markov với thời gian liên tục và trạng thái rời rạc. Để giải quyết tình huống thương lượng, chúng tôi thiết lập điểm bất đồng là cân bằng Nash của vấn đề, sau đó để tìm điểm thỏa thuận mới, chúng tôi tuân theo mô hình thương lượng được trình bày bởi Kalai–Smorodinsky với việc áp dụng khái niệm điểm lý tưởng. Chúng tôi minh họa việc hình thành trò chơi dưới dạng phương trình lập trình phi tuyến bằng cách thực hiện nguyên lý Lagrange. Phương pháp điều chỉnh Tikhonov được áp dụng để đảm bảo sự hội tụ của các hàm chi phí về một điểm cân bằng. Để giải quyết vấn đề, chúng tôi sử dụng một phương pháp lập trình được thực hiện bằng cách tiếp cận tối ưu hóa extraproximal. Phương pháp được đề xuất được xác thực thông qua một ví dụ số liên quan đến vấn đề thị trường lao động cho một bài toán thương lượng ba người.

Accurately identifying changes in carbon trading prices can provide reasonable reference indicators for a government's macrocontrol and can also help companies more effectively avoid risks brought by carbon emissions and increase the income of carbon assets. Based on the Prophet model, LSTM neural network model, and backpropagation (BP), this paper proposes a method to predict carbon trading prices using the ensemble learning model and uses the Hubei carbon trading market data to predict carbon trading prices. Results show that in terms of accuracy, the Prophet-LSTM-BP ensemble learning model achieves better predictive ability than existing models; its RMSE, MAE, and MAPE are 1.479, 0.951, and 2.135, respectively, which are markedly smaller than the Prophet model's 5.631, 4.471, and 9.661, and the LSTM model’s 3.352, 3.105, and 6.880, respectively. Compared with the traditional time series ARIMA model, the MAPE of ARIMA reaches 12.933, which is nearly 1.5 times that of the Prophet model, nearly 2 times that of the LSTM model, and nearly 7 times that of the ensemble learning model. In terms of applicability, when the model is applied to the national carbon trading market, the difference in MAPE compared with the Hubei carbon trading market is only 0.6%, and the other parameters are not more than 16%. The model improves the relevant research on carbon trading price predicting, and concurrently, this method provides ideas for carbon trading price predicting in other carbon trading markets and promotes the sustainable development of the national carbon trading market.

In this paper, we study the long-run wealth distribution of agents with different trading strategies in the framework of the Genoa Artificial Stock Market.The Genoa market is an agent-based simulated market able to reproduce the main stylised facts observed in financial markets, i.e., fat-tailed distribution of returns and volatility clustering. Various populations of traders have been introduced in a`thermal bath' made by random traders who make random buy and sell decisions constrained by the available limited resources and depending on past price volatility. We study both trend following and trend contrarian behaviour; fundamentalist traders (i.e., traders believing that stocks have a fundamental price depending on factors external to the market) are also investigated. Results show that the strategy alone does not allow forecasting which population will prevail. Trading strategies yield different results in different market conditions. Generally, in a closed market (a market with no money creation process), we find that trend followers lose relevance and money to other populations of traders and eventually disappear, whereas in an open market (a market with money inflows), trend followers can survive, but their strategy is less profitable than the strategy of other populations.

In their model of network formation, Berninghaus et al. (Exp Econ 9:237–251, 2006; J Evol Econ 17:317–347, 2007) showed that a periphery-sponsored star network is a strict Nash equilibrium. To examine the validity of their result, they also performed a laboratory experiment with human subjects, and they found that a periphery-sponsored star network can be formed, but when broken down, a different star network forms. In this paper, after considering some factors explaining this phenomenon, we develop a simulation system involving these factors with artificial autonomous agents. Through simulations using this system, we try to explain the behavior of human subjects in the network formation experiment, i.e., the fact that a strict Nash equilibrium periphery-sponsored star network is broken down and a different periphery-sponsored star network is formed.

In this article we examine multicollinearity in the standard OLS interaction-term model—a problem often disregarded by practitioners and in previous research. As a remedy we propose a number of new shrinkage parameters based on the Liu (Commun Stat 22:393–402, 1993) estimator. Using Monte Carlo simulations, we evaluate the robustness of all models for different data-generating processes under varying conditions such as altered sample sizes and error distributions. In the simulation study it is demonstrated that the Liu estimator, which is robust to multicollinearity, systematically outperforms the traditionally applied OLS approach. The simple reason is that interaction models by definition always induce substantial multicollinearity, which in turn distorts the inference of OLS. Conversely, the Liu estimator is robust against multicollinearity in interaction-term models. The advantages of our Liu-based method are also demonstrated in practice when examining the efficiency of the Swedish power retailing market. By the use of this unique data set we find strong evidence of positive asymmetric price transmission effects. Increases in Nord Pool electricity wholesale spot prices lead to immediate and full increases in the electricity retail prices, but decreases in Nord Pool prices are not completely passed down or are delayed before being passed down to consumers. This finding suggests evidence of inefficient and unjust wealth transfers from consumers to retailers in the Swedish power market.

We sketch here the basis of a behavioral theory of non-market decision making or collective action. Departing from the basic social problem, the coordination of individual actions when individual rationality is opposed to collective rationality, we model a population of agents choosing their level of individual cooperation. The social dilemma that emerges may be solved in a bounded rationality evolutionary context. We find that the efficiency embodied in the solutions is dependent on the type of learning individuals adopt. Additional returns to the individual from collective contributions and discounting the future play key roles in the determination of the solution. We conclude that the emergent properties of the social cooperation agree with the findings in the experimental literature: cooperation, although not optimal, is a fact, and institutional settings affect the outcomes in a significant way.