Director independence or decision bias? An investigation of alternative sources of agency costs in board decision making

Research into boards traditionally focuses on independent monitoring of management, with studies focused on the effect of board independence on firm performance. This thesis aims to broaden the research tradition by consolidating prior research and investigating how agents may circumvent independent monitoring. Meta-analysis of previous board independence-firm performance studies indicated no systematic relationship between board independence and firm performance. Next, a series of experiments demonstrated that the presentation of recommendations to directors may bias decision making irrespective of other information presented and the independence of the decision maker. Together, results suggest that independence may be less important than the agent's motivation to misdirect the monitoring process.

Jacob Mestel 1914-1958

Correspondence with individuals: Jacob Ben-Ami, Ossip Dymow, Ovsei Liubomirskii, Kalman Marmor, Nachman Meisel, Melech Ravitch, Dov Sadan, Michael Weichert and Zalman Zylbercweig. Correspondence with organizations: Hebrew Actors' Union, IKUF, YIVO. Manuscripts of plays collected by Mestel as director, including adaptations by Mestel. Mestel's writings: manuscripts of poems, plays, essays, articles, notes, translations. Theater production materials: scripts with Mestel's direction notes, prop and set design notes. Miscellaneous theater materials, including course notes, theater programs. Clippings: Mestel's writings, biographical articles, reviews of performances. Family correspondence and personal papers including papers of Sara Kindman-Mestel. Photographs relating to Yiddish theater in New York, 1930s-1950s.

Appalling behaviour

'Appalling Behaviour' is a critically acclaimed contemporary Australian monologue, written by AWGIE Award winning playwright, Stephen House. This production, directed and creatively adapted by Shane Pike, was presented at the Brisbane Powerhouse in February 2016, as part of Queensland's LGBTIQ festival, Melt. This adaptation of the work experimented with notions of gender, taking the original script and manipulating character and scene to investigate expressions of identity beyond the traditional notions of binary gender-norms. To this end, the sole character (and actor) was (re)presented as a homeless bi-sexual queen with the aim of inferring that gender un/ab-normative characters can exist not only as disruptors/comments on/agitators of traditional expectations of performed gender (both onstage and off), but can also exist as accepted characters in and of themselves. Put simply: can a bi-sexual queen just be an actor/character in a play, or do all gender extra-normative characters inherently exist as political, social and cultural challengers? If so, why is this the case and should we be aiming for this kind of character to be an accepted part of the performative fabric, seamless and fitting within any onstage situation and play (why can't Willy Loman, King Lear or Nora be gender non-normative), or should such (re)presentations always exist as 'different'? Is it time for individual expressions of gender to just 'be' and be accepted as givens, or are we not quite there yet?

(Re)presenting Masculinity: A theatre director's critical observations of, and theatrical experimentation with, (re)presentations of masculinity in selected works of contemporary Australian theatre

Health professionals, academics, social commentators and the media are increasingly sending the same message – Australian men are in crisis. This message has been supported by documented rises in alcoholism, violence, depression, suicide and crime amongst men in Australia. A major cause of this crisis, it can be argued, is an over-reliance on the out-dated and limited model of hegemonic masculinity that all men are encouraged to imitate in their own behaviour. This paper, as part of a larger study, explores representations of masculinity in selected works of contemporary Australian theatre in order to investigate the concept of hegemonic masculinity and any influence it may have on the perceived ‘crisis of masculinity’. Theatre is but one of the artistic modes that can be used to investigate masculinity and issues associated with identity. The Australia Council for the Arts recognises theatre, along with literature, dance, film, television, inter-arts, music and visual arts, as critical to the understanding and expression of Australian culture and identity. Theatre has been chosen in this instance because of the opportunities available to this study for direct access to specific theatre performances and creators and, also, because of the researcher’s experience, as a theatre director, with the dramatic arts. Through interviews with writers, directors and actors, combined with the analysis of scripts, academic writings, reviews, articles, programmes, play rehearsals and workshops, this research utilises theatre as a medium to explore masculinity in Australia.

Energy use in Indian household sector - An actor-oriented approach

Over the years, significant changes have taken place with regard to the type as well the quantity of energy used in Indian households. Many factors have contributed in bringing these changes. These include availability of energy, security of supplies, efficiency of use, cost of device, price of energy carriers, ease of use, and external factors like technological development, introduction of subsidies, and environmental considerations. The present paper presents the pattern of energy consumption in the household sector and analyses the causalities underlying the present usage patterns. It identifies specific (groups of) actors, study their specific situations, analyse the constraints and discusses opportunities for improvement. This can be referred to ``actor-oriented'' analysis in which we understand how various actors of the energy system are making the system work, and what incentives and constraints each of these actors is experiencing. It analyses actor linkages and their impact on the fuel choice mechanism. The study shows that the role of actors in household fuel choice is significant and depends on the level of factors - micro, meso and macro. It is recommended that the development interventions should include actor-oriented tools in energy planning, implementation, monitoring and evaluation. The analysis is based on the data from the national sample survey (NSS), India. This approach provides a spatial viewpoint which permits a clear assessment of the energy carrier choice by the households and the influence of various actors. The scope of the paper is motivated and limited by suggesting and formulating a powerful analytical technique to analyse the problem involving the role of actors in the Indian household sector.

Natural actor-critic algorithms

We present four new reinforcement learning algorithms based on actor-critic, natural-gradient and functi approximation ideas,and we provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function-approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of special interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients. Our results extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.

Parametrized actor-critic algorithms for finite-horizon MDPs

Due to their non-stationarity, finite-horizon Markov decision processes (FH-MDPs) have one probability transition matrix per stage. Thus the curse of dimensionality affects FH-MDPs more severely than infinite-horizon MDPs. We propose two parametrized 'actor-critic' algorithms to compute optimal policies for FH-MDPs. Both algorithms use the two-timescale stochastic approximation technique, thus simultaneously performing gradient search in the parametrized policy space (the 'actor') on a slower timescale and learning the policy gradient (the 'critic') via a faster recursion. This is in contrast to methods where critic recursions learn the cost-to-go proper. We show w.p 1 convergence to a set with the necessary condition for constrained optima. The proposed parameterization is for FHMDPs with compact action sets, although certain exceptions can be handled. Further, a third algorithm for stochastic control of stopping time processes is presented. We explain why current policy evaluation methods do not work as critic to the proposed actor recursion. Simulation results from flow-control in communication networks attest to the performance advantages of all three algorithms.

Jouko Turkan Teatterikorkeakoulun kauden yhteiskunnallinen kontekstuaalisuus vuosina 1982 - 1985

The subject of my doctoral thesis is the social contextuality of Finnish theater director, Jouko Turkka's (b. 1942) educational tenure in the Theater Academy of Finland 1982 1985. Jouko Turkka announced in the opening speech of his rectorship in 1982 that Finnish society had undergone a social shift into a new cultural age, and that actors needed new facilities like capacity, flexibility, and ability for renewal in their work. My sociological research reveals that Turkka adapted cultural practices and norms of new capitalism and new liberalism, and built a performance environment for actors' educational work, a real life simulation of a new capitalist workplace. Actors educational praxis became a cultural performance, a media spectacle. Turkka's tenure became the most commented upon and discussed era in Finnish postwar theater history. The sociological method of my thesis is to compare information of sociological research literature about new capitalist work, and Turkka's educational theater work. In regard to the conceptions of legitimation, time, dynamics, knowledge, and social narrative consubstantial changes occurred simultaneously in both contexts of workplace. I adapt systems and chaos theory's concepts and modules when researching how a theatrical performance self-organizes in a complex social space and the space of Information. Ilya Prigogine's chaos theoretic concept, fluctuation, is the central social and aesthetic concept of my thesis. The chaos theoretic conception of the world was reflected in actors' pedagogy and organizational renewals: the state of far from equilibrium was the prerequisite of creativity and progress. I interpret the social and theater's aesthetical fluctuations as the cultural metaphor of new capitalism. I define the wide cultural feedback created by Turkka's tenure of educational praxis, and ideas adapted from the social context into theater education, as an autopoietic communicative process between theater education and society: as a black box, theater converted the virtual conception of the world into a concrete form of an actor's psychophysical praxis. Theater educational praxis performed socially contextual meanings referring to a subject's position in the social change of 1980s Finland. My other theoretic framework lies close to the American performance theory, with its close ties to the social sciences, and to the tradition of rhetoric and communication: theater's rhetorical utility materializes quotidian cultural practices in a theatrical performance, and helps the audience to research social situations and cultural praxis by mirroring them and creating an explanatory frame.

An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes

We develop in this article the first actor-critic reinforcement learning algorithm with function approximation for a problem of control under multiple inequality constraints. We consider the infinite horizon discounted cost framework in which both the objective and the constraint functions are suitable expected policy-dependent discounted sums of certain sample path functions. We apply the Lagrange multiplier method to handle the inequality constraints. Our algorithm makes use of multi-timescale stochastic approximation and incorporates a temporal difference (TD) critic and an actor that makes a gradient search in the space of policy parameters using efficient simultaneous perturbation stochastic approximation (SPSA) gradient estimates. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal policy. (C) 2010 Elsevier B.V. All rights reserved.

The actor-critic algorithm as multi-time-scale stochastic approximation

The actor-critic algorithm of Barto and others for simulation-based optimization of Markov decision processes is cast as a two time Scale stochastic approximation. Convergence analysis, approximation issues and an example are studied.

Incremental natural-gradient actor-critic algorithms

We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic rein- forcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their com- patibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further re- duce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal differ- ence learning in the actor and by incorporating natural gradients, and they extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.

An Actor-Critic Algorithm for Finite Horizon Markov Decision Processes

We develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication.

A Simultaneous Deterministic Perturbation Actor-Critic Algorithm with an Application to Optimal Mortgage Refinancing

We develop a simulation-based, two-timescale actor-critic algorithm for infinite horizon Markov decision processes with finite state and action spaces, with a discounted reward criterion. The algorithm is of the gradient ascent type and performs a search in the space of stationary randomized policies. The algorithm uses certain simultaneous deterministic perturbation stochastic approximation (SDPSA) gradient estimates for enhanced performance. We show an application of our algorithm on a problem of mortgage refinancing. Our algorithm obtains the optimal refinancing strategies in a computationally efficient manner

An Online Actor-Critic Algorithm with Function Approximation for Constrained Markov Decision Processes

We develop an online actor-critic reinforcement learning algorithm with function approximation for a problem of control under inequality constraints. We consider the long-run average cost Markov decision process (MDP) framework in which both the objective and the constraint functions are suitable policy-dependent long-run averages of certain sample path functions. The Lagrange multiplier method is used to handle the inequality constraints. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal solution. We also provide the results of numerical experiments on a problem of routing in a multi-stage queueing network with constraints on long-run average queue lengths. We observe that our algorithm exhibits good performance on this setting and converges to a feasible point.