Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Banking psychographics: an Indian empirical study

India has been witnessing an economic boom which fuelling a huge growth in the financial sector especially the banks. The spending power and consumerism has been increasing along with the growth in GDP. The numbers of banks are around 3000 (data according to Reserve Bank of India). With a population base of close to 1.1 billion and a diverse culture that has been dictating the mindset and lifestyle of the population, it has been a challenge for the banks to understand the customer better and hence a the need of the hour is a proper psychographic study of retail banking customers.

Jatropha cultivation in southern India: assessing farmers' experiences

Together with 106 farmers who started growing Jatropha (Jatropha curcas L.) in 20042006, this research sought to increase the knowledge around the real-life experience of Jatropha farming in the southern India states of Tamil Nadu and Andhra Pradesh. Launched as an alternative for diesel in India, Jatropha has been promoted as a non-edible plant that could grow on poor soils, yield oil-rich seeds for production of bio-diesel, and not compete directly with food production. Through interviews with the farmers, information was gathered regarding their socio-economic situation, the implementation and performance of their Jatropha plantations, and their reasons for continuing or discontinuing Jatropha cultivation. Results reveal that 82% of the farmers had substituted former cropland for their Jatropha cultivation. By 2010, 85% (n = 90) of the farmers who cultivated Jatropha in 2004 had stopped. Cultivating the crop did not give the economic returns the farmers anticipated, mainly due to a lack of information about the crop and its maintenance during cultivation and due to water scarcity. A majority of the farmers irrigated and applied fertilizer, and even pesticides. Many problems experienced by the farmers were due to limited knowledge about cultivating Jatropha caused by poor planning and implementation of the national Jatropha program. Extension services, subsidies, and other support were not provided as promised. The farmers who continued cultivation had means of income other than Jatropha and held hopes of a future Jatropha market. The lack of market structures, such as purchase agreements and buyers, as well as a low retail price for the seeds, were frequently stated as barriers to Jatropha cultivation. For Jatropha biodiesel to perform well, efforts are needed to improve yield levels and stability through genetic improvements and drought tolerance, as well as agriculture extension services to support adoption of the crop. Government programs will -probably be more effective if implementing biodiesel production is conjoined with stimulating the demand for Jatropha biodiesel. To avoid food-biofuel competition, additional measures may be needed such as land-use restrictions for Jatropha producers and taxes on biofuels or biofuel feedstocks to improve the competitiveness of the food sector compared to the bioenergy sector. (c) 2012 Society of Chemical Industry and John Wiley & Sons, Ltd