What drives the adoption of SHRM in Indian Companies?

Managerial innovation and its critical importance in today's global business is well documented. The crucial role of managerial innovation in strategic human resource management is becoming increasingly prevalent in both business and academic literature. However, practically no such study has been undertaken in an emerging country scenario as India. This study identifies the drivers of adoption of innovative strategic human resource practices (SHRM) in Indian organizations. This study is of critical importance against the backdrop of the liberalization of the Indian economy which started in 1991. The structural adjustments due to liberalization have created a hyper-competitive and turbulent environment. Drawing from both innovation and SHRM literature this research report discusses five main propositions of adoption of innovative SHRM practices in Indian organizations. The generalisability, applicability, acceptability, and the diffusion of practices are discussed.Strategic planning; HRM; Innovation; India

Conditionally Risk-Averse Contextual Bandits

Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonetheless we exhibit the first risk-averse contextual bandit algorithm with an online regret guarantee. We conduct experiments from diverse scenarios where worst-case outcomes should be avoided, from dynamic pricing, inventory management, and self-tuning software; including a production exascale data processing system

‘Short Interest Pressure’ and Competitive Behaviour

This study introduces and examines a new-to-strategy form of Wall Street pressure – ‘short interest pressure’ – the tension felt by management caused by short sales of the firm\u27s stock. Drawing from a sample of over 5000 competitive actions carried out by competing firms over a 6-year time period, we test whether the level of short interest pressure experienced by the firm in one time period is predictive of properties of the firm\u27s competitive action repertoire in the ensuing time period. Our findings suggest that when faced with short interest pressure firms tend to carry out a higher number of competitive actions in the following time period, as well as a set of actions that deviate from the industry norm. In addition, post hoc analysis reveals that this effect is amplified for poorly performing firms. Thus, our study contributes to a deeper understanding of the relationship between capital market signals and competitive strategy

Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards

In this paper, we study the problem of (finite horizon tabular) Markov decision processes (MDPs) with heavy-tailed rewards under the constraint of differential privacy (DP). Compared with the previous studies for private reinforcement learning that typically assume rewards are sampled from some bounded or sub-Gaussian distributions to ensure DP, we consider the setting where reward distributions have only finite $(1+v)$-th moments with some $v \in (0,1]$. By resorting to robust mean estimators for rewards, we first propose two frameworks for heavy-tailed MDPs, i.e., one is for value iteration and another is for policy optimization. Under each framework, we consider both joint differential privacy (JDP) and local differential privacy (LDP) models. Based on our frameworks, we provide regret upper bounds for both JDP and LDP cases and show that the moment of distribution and privacy budget both have significant impacts on regrets. Finally, we establish a lower bound of regret minimization for heavy-tailed MDPs in JDP model by reducing it to the instance-independent lower bound of heavy-tailed multi-armed bandits in DP model. We also show the lower bound for the problem in LDP by adopting some private minimax methods. Our results reveal that there are fundamental differences between the problem of private RL with sub-Gaussian and that with heavy-tailed rewards.Comment: ICML 2023. arXiv admin note: text overlap with arXiv:2009.09052 by other author

Near-Optimal Differentially Private Reinforcement Learning

Motivated by personalized healthcare and other applications involving sensitive data, we study online exploration in reinforcement learning with differential privacy (DP) constraints. Existing work on this problem established that no-regret learning is possible under joint differential privacy (JDP) and local differential privacy (LDP) but did not provide an algorithm with optimal regret. We close this gap for the JDP case by designing an $\epsilon$-JDP algorithm with a regret of $\widetilde{O}(\sqrt{SAH^2T}+S^2AH^3/\epsilon)$ which matches the information-theoretic lower bound of non-private learning for all choices of $\epsilon> S^{1.5}A^{0.5} H^2/\sqrt{T}$. In the above, $S$, $A$ denote the number of states and actions, $H$ denotes the planning horizon, and $T$ is the number of steps. To the best of our knowledge, this is the first private RL algorithm that achieves \emph{privacy for free} asymptotically as $T\rightarrow \infty$. Our techniques -- which could be of independent interest -- include privately releasing Bernstein-type exploration bonuses and an improved method for releasing visitation statistics. The same techniques also imply a slightly improved regret bound for the LDP case.Comment: 38 page