In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multistage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
The authors develop a conceptual framework of the marketing–finance interface and discuss its implications for the theory and practice of marketing. The framework proposes that marketing is concerned with the task of developing and managing market-based assets, or assets that arise from the commingling of the firm with entities in its external environment. Examples of market-based assets include customer relationships, channel relationships, and partner relationships. Market-based assets, in turn, increase shareholder value by accelerating and enhancing cash flows, lowering the volatility and vulnerability of cash flows, and increasing the residual value of cash flows.
Kentaro Ueda, François Portet, Hirohiko Suwa
et al.
While LLMs excel at general tasks, they struggle in specialized domains like finance, requiring diverse skills in domain knowledge, mathematical reasoning, and multilingual processing. Merging domain-specific Continual Pre-training (CPT) "experts" offers a practical alternative to costly and unstable multi-skill training. However, unlike established Supervised Fine-Tuning (SFT) model-based merging, CPT model merging remains largely unexplored. We address this gap by creating financial LLMs from experts in finance, math, and Japanese. We propose a three-stage evaluation focusing on knowledge recovery, complementarity, and emergence, and assess three merging methods (Task Arithmetic, TIES, and DARE-TIES) on a comprehensive financial benchmark curated from 18 tasks across 8 established datasets. Results show that merging an expert with its base model recovers general knowledge lost during CPT, while merging experts improves performance and can yield emergent cross-domain skills. Among the methods, Task Arithmetic performs strongly but is hyperparameter-sensitive, whereas TIES is more robust. Our findings also suggest that while model similarity correlates with merging success, emergent skills depend on more complex factors. This work presents the first foundational analysis of CPT model merging, establishing a principled framework and providing clear guidance for building multi-skill LLMs from existing assets.
The ongoing advancements in synthetic biology, employing either “bottom-up” or “top-down” approaches to construct synthetic life, are generating significant interest. However, the broad application of these scientific practices remains fraught with ethical controversies. Thus, investigating the intrinsic value associated with synthetic life is crucial for determining whether and how synthetic life should be constructed and utilized. This study draws upon and extends Ronald Sandler’s theory of intrinsic value, analyzing the intrinsic subjective value of synthetic life from the perspectives of ecocentrism, human culture, and the structural properties of synthetic life itself. It examines the intrinsic objective value of synthetic life based on its natural purposes. Additionally, the study explores the inherent worth of synthetic life from three angles: biology, subjectivity, and relationships with human beings. We conclude that the intrinsic value of synthetic life increases sequentially from synthetic microorganisms to synthetic plants, synthetic invertebrates, synthetic vertebrates, and synthetic humans. All forms of synthetic life possess intrinsic subjective and objective value. However, only synthetic life above the grade of synthetic microorganisms has inherent worth; thus, humans have moral obligations towards them.