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The consumer demand-price function is the basic theory of the government and enterprises to formulate resource allocation methods, analyze market equilibrium and design pricing strategies, which supports the stability of market supply and the orderly development
of the economy.
Nowadays, with the diversification of consumption patterns, consumer demand data is large-scale and changeable, which brings many practical problems such as the lack of key data and untrue consumer feedback behavior, which makes accurately calculating the demand-price function from a large amount of data a challenging scientific problem
.
With the support of the National Natural Science Foundation of China (grant number: 72150002), a research team composed of Wang Zizhuo of Chinese University of Hong Kong, Shenzhen and Ge Dongdong of Shanghai University of Finance and Economics, together with Professor Chen Xi of New York University, conducted a series of studies
on the demand-price function estimation problem when there is only some aggregated data and consumers may have strategic feedback behavior 。 It is found that the traditional regression analysis model may have deviations in the estimation of the real demand-price function when only the aggregated data, and the research team proposes a modified mapping function to correct the above deviation based on the moment generation function analysis framework, analyzes the relationship between the deviation direction and the characteristics of consumer demand data, and theoretically proves the asymptotic unbias of the method.
The research team proposed a randomized data collection method to reduce the strategic feedback behavior of consumers by reasonably setting the information collection interval and probability to solve the bias, and theoretically proved its effectiveness
through the martingale convergence analysis method.
The above research results are entitled "Aggregation Bias in Estimating Log-Log Demand Functions" and "Bayesian Dynamic Learning and Pricing with Strategic Customers".
It was officially published
in the journal Production and Operations Management in November 2021 and August 2022, respectively 。 The links to papers are: https://onlinelibrary.
wiley.
com/doi/full/10.
1111/poms.
13488 and https://onlinelibrary.
wiley.
com/doi/full/10.
1111/poms.
13741
.
