1,090 research outputs found
A game theoretical approach to homothetic robust forward investment performance processes in stochastic factor models
This paper studies an optimal forward investment problem in an incomplete
market with model uncertainty, in which the dynamics of the underlying stocks
depends on the correlated stochastic factors. The uncertainty stems from the
probability measure chosen by an investor to evaluate the performance. We
obtain directly the representation of the power robust forward performance
process in factor-form by combining the zero-sum stochastic differential game
and ergodic BSDE approach. We also establish the connections with the
risk-sensitive zero-sum stochastic differential games over an infinite horizon
with ergodic payoff criteria, as well as with the classical power robust
expected utility for long time horizons.Comment: 27 page
Stochastic representation for solutions of a system of coupled HJB-Isaacs equations with integral-partial operators
In this paper, we focus on the stochastic representation of a system of
coupled Hamilton-Jacobi-Bellman-Isaacs (HJB-Isaacs (HJBI), for short) equations
which is in fact a system of coupled Isaacs' type integral-partial differential
equation. For this, we introduce an associated zero-sum stochastic differential
game, where the state process is described by a classical stochastic
differential equation (SDE, for short) with jumps, and the cost functional of
recursive type is defined by a new type of backward stochastic differential
equation (BSDE, for short) with two Poisson random measures, whose
wellposedness and a prior estimate as well as the comparison theorem are
investigated for the first time. One of the Poisson random measures appearing
in the SDE and the BSDE stems from the integral term of the HJBI equations; the
other random measure in BSDE is introduced to link the coupling factor of the
HJBI equations. We show through an extension of the dynamic programming
principle that the lower value function of this game problem is the viscosity
solution of the system of our coupled HJBI equations. The uniqueness of the
viscosity solution is also obtained in a space of continuous functions
satisfying certain growth condition. In addition, also the upper value function
of the game is shown to be the solution of the associated system of coupled
Issacs' type of integral-partial differential equations. As a byproduct, we
obtain the existence of the value for the game problem under the well-known
Isaacs' condition.Comment: 37 page
Numerical simulation on the aerodynamic effects of blade icing on small scale Straight-bladed VAWT
AbstractTo invest the effects of blade surface icing on the aerodynamics performance of the straight-bladed vertical-axis wind turbine (SB-VAWT), wind tunnel tests were carried out on a static straight blade using a simple icing wind tunnel. Firstly, the icing situations on blade surface at some kinds of typical attack angle were observed and recorded under different cold water flow fluxes. Then the iced blade airfoils were combined into a SB-VAWT model with two blades. Numerical simulations were carried out on this model, and the static and dynamic torque coefficients of the model with and without icing were computed. Both the static and dynamic torque coefficients were decreased for the icing effects
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
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