Imperfect Decision-making Research Articles

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System.

Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers' imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.

FOOTBALL BETTING AND BEHAVIORAL ECONOMICS: A NOTE

The strategy of oddsmakers to alter point spreads that capitalize on bettors’ preference for popular teams not only dooms regular profits for gamblers but also provides a clear example of imperfect decision-making for behavioral economists. The fear of regret suggests that bettors gravitate toward popular teams since any losses would not be peculiar to them. The implication that spreads on popular college teams would be inflated to balance bets and, in turn, create profit opportunities when betting against popular teams proved faulty.

Experimental thermal/economic/exergetic evaluation of hot/cold water production process by thermoelectricity

Although economic analysis of thermoelectric power generator has been carried out extensively by many researchers, economic reports on the reverse application of thermoelectric (heating/cooling) are completely immature. No experimental-data-based economic evaluation has been carried out before for cooling/heating aim of Peltier effect. Obviously, lack of economic analysis results in incorrect or imperfect decision-making on marketing area of any product. This paper focuses on cold and hot water production by thermoelectricity and provides a comprehensive thermal/economic/exergetic evaluation for said application using recently developed SPECO (Specific Exergy Costing) method. The effect of all thermal/flow and electrical parameters are investigated. The formulation/evaluation process of economic factors is explained step by step to make it ease understanding for the future readers. The results showed that, obtaining for example 1 kWh energy in the form of water cooling or heating is much more cheaper if the input power (DC voltage) of any individual thermoelectric is adjusted in lower value. However, it will take more time to obtain 1 kWh cooling/heating energy when the input power is low. Hence, it is recommended to produce the cold/hot water in no-peak water consumption times with lower input power and store it (instead of cold/hot water production with higher input power through shorter time).

An Online Evolving Framework for Advancing Reinforcement-Learning based Automated Vehicle Control

Abstract In this paper, an online evolving framework is proposed to detect and revise a controller’s imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and controller modules. The e-FSM module evolves a stochastic model (e.g., Discrete-Time Markov Chain) from scratch by determining new states and identifying transition probabilities repeatedly. With the latest stochastic model and given criteria, the action-reviser module checks validity of the controller’s chosen action by predicting future states. Then, if the chosen action is not appropriate, another action is inspected and selected. In order to show the advantage of the proposed framework, the Deep Deterministic Policy Gradient (DDPG) w/ and w/o the online evolving framework are applied to control an ego-vehicle in the car-following scenario where control criteria are set by speed and safety. Experimental results show that inappropriate actions chosen by the DDPG controller are detected and revised appropriately through our proposed framework, resulting in no control failures after a few iterations.

Degrees of Rationality in Agent-Based Retail Markets

The imperfect decision-making of human buyers participating in retail markets varies from fundamental models that assume rational economic choices: even in markets with identical items human buyers are not rational, i.e., buyers do not always choose the cheapest option. Recent developments in artificial intelligence and e-commerce enable market participation by software agents that are (almost) perfectly rational due to their computational capacity. However, the increasing degree of buyers’ rationality might have unfavorable effects on retail markets with regards to the competition between sellers and the resulting prices. In this paper, we study the effects of varying degrees of buyers’ rationality on the competition and the prices buyers face in retail markets with identical items. We use the multinomial logit function to model different degrees of buyers’ rationality. We further model the competition between sellers using k-level reasoning: each seller computes the price to offer (best response strategy) with regards to its belief for the competition. First, we derive an analytical best response strategy (price) of a seller given the competing prices and the degree of buyers’ rationality, and show that there exists an optimal degree of buyers’ rationality that minimizes the price. Last, we use evolutionary game theory to show that perfect rationality leads to unstable competition dynamics increasing the overall cost for buyers. In contrast, bounded rationality leads to smoother dynamics and lower cost for buyers. Our insights raise the need to revisit design objectives for software agents in retail markets in light of their wider systematic impact.

The Necessary Complexity of Predatory Pricing Analysis

While I agree with much of what Markovits writes about predatory pricing, I focus on two main areas of disagreement. First, tests for predatory pricing should reflect an optimal balance of deterrence of anticompetitive predation while minimizing the chilling of procompetitive price competition, whether or not this maximizes the accuracy of the tests. Second, while liability based on subjective beliefs (not intent) about predation generate optimal incentives if courts are perfect, an objective test might accomplish this better due to imperfect decision-making. I also expand on a point which Markovits makes with which I agree and is of fundamental importance. Quick screens for predatory pricing are only valuable if they are both substantially less costly than directly evaluating the deterrence/chilling balance and are not substantially worse at achieving this balance. Most tests for predatory pricing, such as the commonly used price-cost test, do not clearly satisfy these criteria.

Models of complex adaptive systems in strategy and organization research

The development of new theory is often spurred by novel techniques that provide better answers to existing questions, or that allow asking new ones. In the field of strategy and organization science, models of complex adaptive systems have renewed theoretical work on a fundamental question: how organizations can adapt effectively to their environments. This article has three objectives: (1) to highlight some areas where models of organizations as complex adaptive systems have made substantial contributions: the search for solutions to sets of interdependent choices, the challenge of balancing processes of exploration and exploitation, and the organization of imperfect decision makers; (2) to point to several challenges and tradeoffs which can limit the explanatory power and eventual impact of the modeling enterprise; and (3) to sketch out possible future directions of research that would do further justice to the notion of organizations as complex adaptive systems.

Models of Complex Adaptive Systems in Strategy and Organization Research

The development of new theory is often spurred by novel techniques that provide better answers to existing questions, or that allow asking new ones. In the field of strategy and organization science, models of complex adaptive systems have renewed theoretical work on a fundamental question: how organizations can adapt effectively to their environments. This article has three objectives: (1) to highlight some areas where models of organizations as complex adaptive systems have made substantial contributions: the search for solutions to sets of interdependent choices, the challenge of balancing processes of exploration and exploitation, and the organization of imperfect decision makers; (2) to point to several challenges and tradeoffs, which can limit the explanatory power and eventual impact of the modeling enterprise; and (3) to sketch out possible future directions of research that would do further justice to the notion of organizations as complex adaptive systems.

Meta‐Reasoning and Practical Deliberation

Fallibilism about our practical judgments is uncontroversial: we are frequently wrong when we make decisions about what to do often badly wrong and everyone knows it. But efforts to characterize and respond to our lamentably imperfect decision-making have lagged. There has been some work in related fields. A few philosophers have tackled our uncertainty about moral principles and the norms that apply in virtue of that uncertainty, or argued that some moral views are better hedges against our fallibility than others.1 In a related vein, there has been discussion of how to respond to evidence of our fallibility emanating from empirical psychology and disagreement among peers.2 But despite this progress toward taking our fallibility as practical (and epistemic) agents seriously, much more work remains to be done, and my aim here is to contribute to this task. In particular, sometimes there is evidence about what we would decide to do from an improved deliberative position one in which we have better information, say, or are subject to less bias, or are able to consider the relevant facts with greater vividness. I argue that in such situations we should act on that evidence, and that there are some important ethical and prudential applications for this idea. Following through with this suggestion allows us to respond to the fact that we are prone to error by making the appropriate

Sociability leads to instability

We present a general stochastic model showing that colonial breeding can lead to complex multi-colony population dynamics when combined with nothing more than (inevitably) imperfect decision-making by individuals. In particular, frequent “switching cascades”—mass movement of individuals between locations from one breeding season to the next—arise naturally from our model, bringing into question the need to invoke a separate, fitness-based explanation for this commonly observed real-world phenomenon. A key component of the model is the development, at the beginning of each breeding season, of a set of colonies, based on sequential choices by individuals about where to breed. Individuals favor the colony they bred in previously, but are also attracted to colonies that are rapidly establishing, and may switch locations. This provides a positive feedback that leads to switching cascades. We examine the effect on the dynamics of individuals’ access to (and ability to act on) information, as well as the overall size of the colony system and of individual colonies. We compare the model’s dynamics to the observed population dynamics of a set of heron and egret breeding colonies in New York Harbor.

A consumption model with separability and imperfect decision makers

A consumption model is considered, where agents may have a simplified image of reality and a simplified way of reaching decisions. Some rules of behaviour are assumed, considering, in particular, the decision-theoretic ideas of decentralization and price aggregation. The consumers have a price-independent relative income distribution. The existence and unicity of temporary equilibria are investigated.