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This paper studies the relationship between search effort and workers' ranking by employers. In order to do so, we propose a matching model in which employers have common preferences over a continuum of heterogeneous workers who c...
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This paper studies the relationship between search effort and workers' ranking by employers. In order to do so, we propose a matching model in which employers have common preferences over a continuum of heterogeneous workers who choose a number of applications to send out. We show that in equilibrium, the relationship is hump-shaped for sufficiently high vacancy-to-worker ratios, that is, highly-ranked and lowly-ranked workers send out fewer applications than workers of mid-range rank. This arises due to two opposing forces driving the incentives of applicants. Increasing the number of applications acts as insurance against unemployment, but is less effective when the probability of success for each application is low. This mechanism exacerbates the negative employment outcomes of low-rank workers-hence, in contrast to the market equilibrium, in the social planner's solution, the number of applications monotonically decrease in rank.
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The literature offers two interpretations of competitive search equilibrium, one based on a Nash approach and the other on a market-maker approach. When each buyer visits only one seller, the two approaches are equivalent. However...
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The literature offers two interpretations of competitive search equilibrium, one based on a Nash approach and the other on a market-maker approach. When each buyer visits only one seller, the two approaches are equivalent. However, when each buyer visits multiple sellers, this equivalence can break down. We present a model in which every buyer visits 2 sellers. A buyer who trades with one seller receives a value of s, while a buyer who trades with 2 sellers receives value 1. Letting s vary from 0 (perfect complements) to 1 (perfect substitutes) we characterize the competitive search equilibrium under the two interpretations. We show that for low values of s, the Nash and market-maker competitive search equilibria coincide, but the common equilibrium is inefficient. For intermediate values of s, the two equilibria again coincide and are efficient. Finally, for high values of s, the Nash and market-maker equilibria differ, and only the latter is efficient.(c) 2023 Elsevier Inc. All rights reserved.
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We study the identification of the search method consumers use when resolving uncertainty in the prices of alternatives. We show that the search method-simultaneous or sequential-is identified with data on consumers' consideration...
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We study the identification of the search method consumers use when resolving uncertainty in the prices of alternatives. We show that the search method-simultaneous or sequential-is identified with data on consumers' consideration sets (but not the sequence of searches), prices for the considered alternatives and marketwide price distributions. We show that identification comes from differences in the patterns of actual prices in consumers' consideration sets across search methods. We also provide a new estimation approach for the sequential search model that uses such data. Using data on consumer shopping behavior in the U.S. auto insurance industry that contain information on consideration sets and choices, we find that the pattern of actual prices in consumers' consideration sets is consistent with consumers searching simultaneously. Via counterfactuals we show that the consideration set and purchase market shares of the largest insurance companies are overpredicted under the incorrect assumption of sequential search. As the search method affects consumers' consideration sets, which in turn influence brand choices, understanding the nature of consumer search and its implications for consideration and choice is important from a managerial perspective.
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Search, broadly defined, is a critical managerial activity. Our contribution is a model of search for multiattribute alternatives, and our focus is on parallel search, where the decision is about the number of alternatives to expl...
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Search, broadly defined, is a critical managerial activity. Our contribution is a model of search for multiattribute alternatives, and our focus is on parallel search, where the decision is about the number of alternatives to explore. Most of the search literature considers univariate alternatives, and it can be applied to a multiattribute setting provided that the trade-offs to be used at the final selection stage were known at the search stage. However, uncertainty about trade-offs is likely to occur, especially in settings that involve parallel search (e.g., vendor selection, new product development, innovation tournaments). We show that incorporating uncertainty about trade-offs into a model changes its search strategy recommendations. Failing to account for such uncertainty, which is likely in practice, leads to suboptimal search and potentially large losses. For parallel search and a multivariate elliptical (e.g., normal) distribution of the alternatives, the solution is equivalent to univariate search with appropriately adjusted standard deviation. We prove that, in this setting, the optimal number of alternatives to explore increases if uncertainty about trade-offs increases, and we discuss the value of information about uncertain trade-offs.
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In practice, firms face a number of scarce innovation projects. They choose one towards which to direct their effort, but do not coordinate these choices. This gives rise to coordination frictions. This paper develops an expanding...
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In practice, firms face a number of scarce innovation projects. They choose one towards which to direct their effort, but do not coordinate these choices. This gives rise to coordination frictions. This paper develops an expanding-variety endogenous growth model to study the implications of these frictions for growth and welfare.We find that the coordination failure generates a number of foregone innovations and reduces the economy-wide research intensity. Both effects decrease the growth rate. This creates a general equilibrium effect that endogenously amplifies the fraction of wasteful simultaneous innovation. Furthermore, formalizing the coordination frictions uncovers a novel link between the “stepping on toes” and “standing on shoulders” externalities—their magnitudes are endogenously determined through the ratio of firms to innovation projects. We find that the “stepping on toes” externality is larger for all parameter values.
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Machines and automated guided vehicles (AGVs) scheduling problems are two essential issues that need to be addressed for the efficiency of the overall production system. The purpose of this paper is to study the simultaneous sched...
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Machines and automated guided vehicles (AGVs) scheduling problems are two essential issues that need to be addressed for the efficiency of the overall production system. The purpose of this paper is to study the simultaneous scheduling problem of machines and AGVs in a flexible manufacturing system (FMS) since the global optimum cannot be reached by considering each of them individually. In this paper, a mixed integer linear programming (MILP) model is developed with the objective of makespan minimisation. The MILP model consists of the following two constraint sets: machines and AGVs scheduling sub-problems. As both sub-problems are known to be NP-hard, a heuristic algorithm based on tabu search (TS) is proposed to get optimal or near to optimal solution for large-size problems within reasonable computation time. The proposed algorithm includes a novel two-dimensional solution representation and the generation of two neighbour solutions, which are alternately and iteratively applied to improve solutions. Moreover, an improved lower bound calculation method is introduced for the large-size problems. Computational results show the superior performance of the TS algorithm for the simultaneous scheduling problem.
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In tracking and geometrical vision, there are usually priors available on the image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search. Much less image p...
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In tracking and geometrical vision, there are usually priors available on the image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search. Much less image processing and lower overall computational cost can be expected for getting global matchings. First, the concept of dynamic sequential search (DSS) is presented. Then, the problem of determining an optimal search order for DSS is investigated, when the probabilistic distribution of the features can be described by a multivariate Gaussian model. Based upon the general formulas for sequentially updating the predicted positions of the features as well as their innovation covariance, the theoretic lower bound for the sum of the areas of the features' search-regions is derived, and the necessary and sufficient condition for the optimal search order to approach this lower bound is presented. After that, an algorithm for dynamically determining a suboptimal search order is presented, with a computational complexity of $Oleft(n^{3}right)$ , which is two magnitudes lower than those of the state-of-the-art algorithms. The effectiveness of the proposed method is validated by both statistical simulation and real-world experiments with a monocular visual SLAM (simultaneous localization and mapping) system. The results verify that the performance of the proposed method is better than the state-of-the-art algorithms, with both fewer image processing operations and lower overall computational cost.
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This article addresses a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up service (VRPSPD). The objective of this problem is to determine the optimal set of routes to totally s...
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This article addresses a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up service (VRPSPD). The objective of this problem is to determine the optimal set of routes to totally satisfy both the delivery and pick-up demand of the customer population. VRPSPD is an NP-hard combinatorial optimization problem; therefore exact methods are incapable of dealing with large scale VRPSPD instances arising in a wide variety of practical operations. We propose a hybrid solution approach incorporating the rationalc of two well-known metaheuristics which have proven to be effective for routing problem variants, namely tabu search and guided local search. The intelligence of the proposed hybrid was designed to achieve a vast exploration of the search space, by escaping from local optima and intensifying at promising solution regions. The performance of our metaheuristic algorithm was tested on benchmark instances involving from 50 to 400 customers. It produced high quality results, improving several best solutions previously reported.
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This paper applies a log-linear simultaneous equation model to estimate the returns to scale in a Cobb-Douglas-type bilateral searching and meeting function which characterises the search frictions between taxis and customers. Num...
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This paper applies a log-linear simultaneous equation model to estimate the returns to scale in a Cobb-Douglas-type bilateral searching and meeting function which characterises the search frictions between taxis and customers. Number of taxis, taxi fare, population, average occupied taxi journey time and disposable income are used as exogenous variables; customer demand, average customer waiting time and average taixi waiting and cruising time in search of each customer are used as endogenous variables. These variables are coupled together through the simultaneous equation model whose parameters are estimated from the Hong Kong Annual Taxi Survey data as well as data from Traffic and Transport Digest and Annual Digest of Statistics. Our calibration results show that urban taxi services in Hong Kong exhibit an increasing return to scale. The established simultaneous equation model can be used to obtain regulatory information useful for decision-making, such as the impact of the restriction over the number of taxi licenses and the fixing of taxi fare structure.
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Aiming at the shortcomings of A* algorithm in the path planning process of electric disinfection vehicles, such as long planned paths, low one-way search efficiency, and unsmooth inflection points, an improved two-way A* search al...
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Aiming at the shortcomings of A* algorithm in the path planning process of electric disinfection vehicles, such as long planned paths, low one-way search efficiency, and unsmooth inflection points, an improved two-way A* search algorithm is proposed from the heuristic function and search direction. First, the cosine factor is introduced into the heuristic function of the A* algorithm to optimize the direction of the path search. Second, the search neighborhood is expanded, and the 24-neighbor search is used to obtain a better path. Finally, the one-way search from the start point to the end point is changed to a two-way simultaneous search. The experimental results show that the path distance planned by the improved A* algorithm is shortened by 9.13%, the time is shortened by 38.21%, and the inflection point is reduced by an average of 30.34%.
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