Modeling Subjective Experience-Based Learning under Uncertainty and Frames

In this paper we computationally examine how subjective experience may help or harm the decision maker's learning under uncertain outcomes, frames and their interactions. To model subjective experience, we propose the "experienced-utility function" based on a prospect theory (PT)-based parameterized subjective value function. Our analysis and simulations of two-armed bandit tasks present that the task domain (underlying outcome distributions) and framing (reference point selection) influence experienced utilities and in turn, the "subjective discriminability" of choices under uncertainty. Experiments demonstrate that subjective discriminability improves on objective discriminability by the use of the experienced-utility function with appropriate framing for a given task domain, and that bigger subjective discriminability leads to more optimal decisions in learning under uncertainty.Massachusetts Institute of Technology. Media Laborator

Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way¿relative to its role, meaning, and relationship with participants in decision making¿because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water managemen

From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning

Video captioning in essential is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, etc. In this paper we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing methods, that most of the decoders propagate deterministic hidden states. Such complex uncertainty cannot be modeled efficiently by the deterministic models. In this paper, we propose a generative approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which models the uncertainty observed in the data using latent stochastic variables. Therefore, MS-RNN can improve the performance of video captioning, and generate multiple sentences to describe a video considering different random factors. Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with both visual and textual features to capture a high-level representation. Then, a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty propagation by introducing latent variables. Experimental results on the challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks

Human Nature in the Adaptation of Trust

This chapter pleads for more inspiration from human nature, in agent-based modeling.As an illustration of an effort in that direction, it summarizes and discusses an agentbased model of the build-up and adaptation of trust between multiple producers and suppliers.The central question is whether, and under what conditions, trust and loyalty are viable in markets.While the model incorporates some well known behavioural phenomena from the trust literature, more extended modeling of human nature is called for.The chapter explores a line of further research on the basis of notions of mental framing and frame switching on the basis of relational signaling, derived from social psychology.trust;transaction costs;buyer-supplier relationships;social psychology

Inertia and Incentives: Bridging Organizational Economics and Organizational Theory

Organizational theorists have long acknowledged the importance of the formal and informal incentives facing a firm%u2019s employees, stressing that the political economy of a firm plays a major role in shaping organizational life and firm behavior. Yet the detailed study of incentive systems has traditionally been left in the hands of (organizational) economists, with most organizational theorists focusing their attention on critical problems in culture, network structure, framing and so on -- in essence, the social context in which economics and incentive systems are embedded. We argue that this separation of domains is problematic. The economics literature, for example, is unable to explain why organizations should find it difficult to change incentive structures in the face of environmental change, while the organizational literature focuses heavily on the role of inertia as sources of organizational rigidity. Drawing on recent research on incentives in organizational economics and on cognition in organizational theory, we build a framework for the analysis of incentives that highlights the ways in which incentives and cognition -- while being analytically distinct concepts -- are phenomenologically deeply intertwined. We suggest that incentives and cognition coevolve so that organizational competencies or routines are as much about building knowledge of %u201Cwhat should be rewarded%u201D as they are about %u201Cwhat should be done.%u201D

Participation in multicriteria decision support - the case of conflicting water allocation in the Spree River basin

This discussion paper presents the Integrated Methodological Approach for participatory multi-criteria decision support under uncertainty (IMA), which emerged from the debates about participation, multi-criteria analysis (MCA) and benefit-cost analysis (BCA). It provides a framework for participatory and science-based evaluation processes with combined use of BCA and MCA to support large-scale public decisions. While IMA does not claim to realize an all-inclusive participation scheme, it offers the advantage to improve the quality of decision making through advances in competence and fairness. Its practical application with emphasis on its participatory elements is demonstrated by the case study on the water allocation conflict of the German Spree River, which involves the German capital of Berlin, an important wetland, and the needs to remediate a post-mining landscape. --Participation,Multi-criteria analysis,Cost-benefit analysis,River basin management,Integrated Assesment