Transferable Utility Games with Uncertainty

We introduce the concept of a TUU-game, a transferable utility game with uncertainty. In a TUU-game there is uncertainty regarding the payoffs of coalitions. One out of a finite number of states of nature materializes and conditional on the state, the players are involved in a particular transferable utility game. We consider the case without ex ante commitment possibilities and propose the Weak Sequential Core as a solution concept. We characterize the Weak Sequential Core and show that it is non-empty if all ex post TUgames are convex

Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks

Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and collects features with rich discriminative information for classification tasks in an observed list. This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the observed list. We hypothesise that the best discriminative features of a classification task carry its characteristics. Therefore, the relatedness between any two tasks can be estimated by comparing their most appropriate patterns. We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs. This system utilises the observed list to estimate the task relatedness. This method enables the automation of transferring features. In terms of knowledge discovery, the resemblance estimation provides insightful relations among multiple data. We experimented mXOF on various scenarios, e.g. representative Hierarchical Boolean problems, classification of distinct classes in the UCI Zoo dataset, and unrelated tasks, to validate its abilities of automatic knowledge-transfer and estimating task relatedness. Results show that mXOF can estimate the relatedness reasonably between multiple tasks to aid the learning performance with the dynamic feature transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference (GECCO 2020

FINANCIAL INTERMEDIATION, ENTREPRENEURSHIP AND ECONOMIC GROWTH

This paper presents a simple general equilibrium model of financial intermediation, entrepreneurship and economic growth. In this model, the role of financial intermediation is to pool savings and to lend the pooled funds to an entrepreneur, who in turn invests the funds in a new production technology. The adoption of the new production technology improves individual real income. Thus financial intermediation promotes economic growth through affecting individuals’ saving behaviour and enabling the adoption of a new production technology.financial intermediation, entrepreneurship, economic growth

Stochastic bankruptcy games

We study bankruptcy games where the estate and the claims have stochastic values. We use the Weak Sequential Core as the solution concept for such games. We test the stability of a number of well known division rules in this stochastic setting and find that most of them are unstable, except for the Constrained Equal Awards rule, which is the only one belonging to the Weak Sequential Core

Signalling to dispersed shareholders and corporate control

This article analyses how outsiders, such as bidders or activist investors, overcome the lack of coordination and information among dispersed shareholders. We identify the two basic means to achieve this goal. First, the outsider must relinquish private benefits in a manner that is informative about security benefits. We show under which conditions this is feasible and which acquisition strategies used in practice meet these conditions. Second, the outsider can alternatively use derivatives to drive a wedge between her voting power and her economic interest in the firm. Such unbundling of ownership and control, while typically considered a source of corporate governance problems, is an efficient response to the frictions dispersed ownership causes for control contestability. We also show that unbundling comes with costs and benefits for the bidder's incentives to improve firm value

Domain Conditioned Adaptation Network

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.Comment: Accepted by AAAI 202