Valuation equilibrium

We introduce a new solution concept for games in extensive form with perfect information, valuation equilibrium, which is based on a partition of each player's moves into similarity classes. A valuation of a player'is a real-valued function on the set of her similarity classes. In this equilibrium each player's strategy is optimal in the sense that at each of her nodes, a player chooses a move that belongs to a class with maximum valuation. The valuation of each player is consistent with the strategy profile in the sense that the valuation of a similarity class is the player's expected payoff, given that the path (induced by the strategy profile) intersects the similarity class. The solution concept is applied to decision problems and multi-player extensive form games. It is contrasted with existing solution concepts. The valuation approach is next applied to stopping games, in which non-terminal moves form a single similarity class, and we note that the behaviors obtained echo some biases observed experimentally. Finally, we tentatively suggest a way of endogenizing the similarity partitions in which moves are categorized according to how well they perform relative to the expected equilibrium value, interpreted as the aspiration level

Imperfect Decision-Making and the Tax Payer Puzzle

Even if the expected punishment on tax evasion is negligible, empirical studies show that actual tax evasion is smaller than rational choice models predict. In addition to this, tax payer do not respond on parameter changes as predicted. Some authors tried to explain this puzzle by assuming "tax morale". Our paper models tax payers as imperfect decision-makers and explains deviations from the optimal solution by making use of a weaker assumption: The imperfect tax payer's decision to deviate from a given rule depends on their competence and on the complexity of their situation. --tax compliance,bounded rationality,imperfect decision-making,detection skill,rule-governed behavior

Evaluation of Uncertain International Markets The Advantage of Flexible Organization Structures

The present article is concerned with organizational flexibility in transnational corporations (TNCs), i.e., larger firms that operate in multiple national markets. Contrasting prior research into entry modes (e.g. joint ventures, greenfield investments, or acquisitions), the present article examines the way the organization of evaluation teams can influence entry and exit decisions of business units. Empirical studies broadly support the claim that TNCs experiment with flexible organizational structures in response to increased levels of turbulence and uncertainty in international markets. However, these advances in the description of TNCs, and more generally in the literature on new organizational forms, have been largely ignored in our theories about evaluation of market opportunities in TNCs and multi-national corporations (MNCs). To address this gap in our knowledge, the present article examines the effects of flexible evaluation teams when TNCs assess the viability of international markets characterized by high levels of uncertainty. Remarkably, we show that TNCs employing flexible teams of (very) fallible evaluators can obtain profits at levels that asymptote optimality. Our main result supports the claim advanced in recent empirical studies. Structural flexibility can help TNCs employing (very) fallible evaluators achieve high levels of performance in conditions of turbulence and uncertainty.Multinational corporations, entry modes

After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study

After-sales maintenance services can be a very profitable source of incomes for original equipment manufacturers (OEM) due to the increasing interest of assets’ users on performance-based contracts. However, when it concerns the product value-adding process, OEM have traditionally been more focused on improving their production processes, rather than on complementing their products by offering after-sales services; consequently leading to difficulties in offering them efficiently. Furthermore, both due to the high uncertainty of the assets’ behaviour and the inherent challenges of managing the maintenance process (e.g. maintenance strategy to be followed or resources to be deployed), it is complex to make business out of the provision of after-sales services. With the aim of helping the business and maintenance decision makers at this point, this paper proposes a framework for optimising the incomes of after-sales maintenance services through: 1) implementing advanced multi-objective opportunistic maintenance strategies that sistematically consider the assets’ operational context in order to perform preventive maintenance during most favourable conditions, 2) considering the specific OEMs’ and users’ needs, and 3) assessing both internal and external uncertainties that might condition the after-sales services’ success. The developed case study for the wind energy sector demonstrates the suitability of the presented framework for optimising the after-sales services.EU Framework Programme Horizon 2020, MSCA-RISE-2014: Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) (grant agreement number 645733- Sustain-Owner-H2020-MSCA-RISE-2014) and the EmaitekPlus 2016-2017 Program of the Basque Government

Strategies for prediction under imperfect monitoring

We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense that they achieve, asymptotically, the best possible average reward. It was Rustichini (1999) who first proved the existence of such consistent predictors. The forecasters presented here offer the first constructive proof of consistency. Moreover, the proposed algorithms are computationally efficient. We also establish upper bounds for the rates of convergence. In the case of deterministic feedback, these rates are optimal up to logarithmic terms.Comment: Journal version of a COLT conference pape

Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment

Imperfect measurement of uncertainty (deeper uncertainty) in climate sensitivity is introduced in a two-sectoral integrated assessment model (IAM) with endogenous growth, based on an extension of DICE. The household expresses ambiguity aversion and can use robust control via a `shadow ambiguity premium' on social carbon cost to identify robust climate policy feedback rules that work well over a range such as the IPCC climate sensitivity range (IPCC, 2007a). Ambiguity aversion, in combination with linear damage, increases carbon cost in a similar way as a low pure rate of time preference. However, ambiguity aversion in combination with non-linear damage would also make policy more responsive to changes in climate data observations. Perfect ambiguity aversion results in an infinite expected shadow carbon cost and a zero carbon consumption path. Dynamic programming identifies an analytically tractable solution to the IAM.climate policy, carbon cost, robust control, Knightian uncertainty, ambiguity aversion, integrated asssessment

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