Session 3-4-C: Are Lottery Players Affected by Random Shocks? Evidence from China’s Individual Lottery Betting Panel Data

I explore a unique, individual level, lottery betting panel data and show that lottery gambling is significantly affected by lottery winning history even though this winning history is shown to be merely an exogenous random shock. This panel data records lottery players’ collective lottery betting behaviors on a Chinese online lottery purchase website. This website lists each player’s lottery investment performance, the ratio between the lottery return and the lottery investment in the past three months, for lottery players’ reference and this ratio is shown to be an independent random shock across players. Based on the data with around 400,000 observations, I find that this random shock significantly affects lottery players’ purchasing behaviors. Specifically, collective lottery gamblers are significantly more likely to join a lottery package proposed by someone with a higher winning rate; lottery players are spending more money on the proposers with higher return rates

The Merits of Sharing a Ride

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads

State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning

In the framework of MDP, although the general reward function takes three arguments-current state, action, and successor state; it is often simplified to a function of two arguments-current state and action. The former is called a transition-based reward function, whereas the latter is called a state-based reward function. When the objective involves the expected cumulative reward only, this simplification works perfectly. However, when the objective is risk-sensitive, this simplification leads to an incorrect value. We present state-augmentation transformations (SATs), which preserve the reward sequences as well as the reward distributions and the optimal policy in risk-sensitive reinforcement learning. In risk-sensitive scenarios, firstly we prove that, for every MDP with a stochastic transition-based reward function, there exists an MDP with a deterministic state-based reward function, such that for any given (randomized) policy for the first MDP, there exists a corresponding policy for the second MDP, such that both Markov reward processes share the same reward sequence. Secondly we illustrate that two situations require the proposed SATs in an inventory control problem. One could be using Q-learning (or other learning methods) on MDPs with transition-based reward functions, and the other could be using methods, which are for the Markov processes with a deterministic state-based reward functions, on the Markov processes with general reward functions. We show the advantage of the SATs by considering Value-at-Risk as an example, which is a risk measure on the reward distribution instead of the measures (such as mean and variance) of the distribution. We illustrate the error in the reward distribution estimation from the direct use of Q-learning, and show how the SATs enable a variance formula to work on Markov processes with general reward functions

Functional Bandits

We introduce the functional bandit problem, where the objective is to find an arm that optimises a known functional of the unknown arm-reward distributions. These problems arise in many settings such as maximum entropy methods in natural language processing, and risk-averse decision-making, but current best-arm identification techniques fail in these domains. We propose a new approach, that combines functional estimation and arm elimination, to tackle this problem. This method achieves provably efficient performance guarantees. In addition, we illustrate this method on a number of important functionals in risk management and information theory, and refine our generic theoretical results in those cases

Economic Transition and the Motherhood Wage Penalty in Urban China: Investigation using Panel Data

China’s economic transition has fundamentally changed the mechanisms for allocating and compensating labor. This paper investigates how the economic transition has affected the wage gap between mothers and childless women in urban China using panel data for the period 1990-2005. The results show that overall, mothers earned considerably less than childless women; additionally, the wage penalties for motherhood went up substantially from the gradualist reform period (1990-1996) to the radical reform period (1999-2005). The results also show that that although motherhood does not appear to have a significant wage effect for the state sector, it imposes substantial wage losses for mothers in the non-state sector. These findings suggest that the economic transition has shifted part of the cost of child-bearing and -rearing from the state and employers back to women in the form of lower earnings for working mothers.