Location (Hotelling) Games and Applications

Brenner, Steffen

doi:10.1002/9780470400531.eorms0477

Cited by 17 publications

(13 citation statements)

References 40 publications

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“…The following overview is by no means exhaustive, see for instance Drezner and Hamacher [11] and Nickel and Puerto [34] for broader surveys on location theory and applications. Hotelling models are also relevant in which they address network facility location problems in competitive environments, see the overviews of Brenner [7] and Pinto and Parreria [36].…”

Section: Figure 1 Illustration Of Branches and Customers Of The Micro Finance Institutionmentioning

confidence: 99%

“…The travelling distance to each territory center is not allowed to be larger than a threshold value, see Equation (6). The total number of each branch type is to be between pre-specified minimum and maximum levels via Equation (7). The pairs of BUs that need to be assigned to different territories due to organizational preferences or geographical issues (e.g., rivers or mountains) are modeled explicitly as hard constraints via Equation (8).…”

Section: Figure 1 Illustration Of Branches and Customers Of The Micro Finance Institutionmentioning

confidence: 99%

See 1 more Smart Citation

Risk-balanced territory design optimization for a Micro finance institution

Pérez¹,

Ekin²,

Jimenez³

et al. 2020

Journal of Industrial &Amp; Management Optimization

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Micro finance institutions (MFIs) play an important role in emerging economies as part of programs that aim to reduce income inequality and poverty. A territory design that balances the risk of branches is important for the profitability and long-term sustainability of a MFI. In order to address such particular business needs, this paper proposes a novel risk-balanced territory planning model for a MFI. The proposed mixed integer programming model lets the MFI choose the location of the branches to be designated as territory centers and allocate the customers to these centers with respect to planning criteria such as the total workload, monetary amount of loans and profit allocation while balancing the territory risk. This model is solved using a branch and cut based hybrid-heuristic framework. We discuss the impact of the risk balancing and merits of the proposed model.

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Section: Figure 1 Illustration Of Branches and Customers Of The Micro Finance Institutionmentioning

confidence: 99%

Section: Figure 1 Illustration Of Branches and Customers Of The Micro Finance Institutionmentioning

confidence: 99%

Risk-balanced territory design optimization for a Micro finance institution

Pérez¹,

Ekin²,

Jimenez³

et al. 2020

Journal of Industrial &Amp; Management Optimization

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“…Facility location games (Anderson, De Palma, and Thisse 1992;Brenner 2010;Fournier and Scarsini 2014), which are extensively studied in economics, operations research and computer science, portray recommendation systems with strategic content providers incredibly well, due to the nature of recommendation systems described above. This is true since in many (and perhaps even most) recommendation systems, the computational task of matching users with content is carried out by modeling both into a joint metric space.…”

Section: The Connection To Facility Location Gamesmentioning

confidence: 99%

From Recommendation Systems to Facility Location Games

Ben-Porat

Goren

Rosenberg

et al. 2019

AAAI

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Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the closest content, where both users and contents are modeled as points in some semantic space, may yield low social welfare. This is due to the fact that content providers are strategic and optimize their offered content to be recommended to as many users as possible. Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers. Our conceptual contribution is the introduction of a mediator to facility location models, in the pursuit of better social welfare. We aim at designing mediators that a) induce a game with high social welfare in equilibrium, and b) intervene as little as possible. In service of the latter, we introduce the notion of intervention cost, which quantifies how much damage a mediator may cause to the social welfare when an off-equilibrium profile is adopted. As a case study in high-welfare low-intervention mediator design, we consider the one-dimensional segment as the user domain. We propose a mediator that implements the socially optimal strategy profile as the unique equilibrium profile, and show a tight bound on its intervention cost. Ultimately, we consider some extensions, and highlight open questions for the general agenda.

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“…Eiselt, Laporte and Thisse [13] provide an extensive comparison of the different models classified by the following characteristics: the number of players, the location space (e.g., circle, plane, network), the pricing policy, the behavior of players, and the behavior of clients. (For more recent surveys see Eiselt et al [12] and Brenner [4].) Osborne et al [29] showed that in many variants of the political setting, no Nash equilibrium exists for more than 2 players.…”

Section: Related Workmentioning

confidence: 99%

Hotelling Games with Random Tolerance Intervals

Cohen

Peleg

2019

Web and Internet Economics

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The classical Hotelling game is played on a line segment whose points represent uniformly distributed clients. The n players of the game are servers who need to place themselves on the line segment, and once this is done, each client gets served by the player closest to it. The goal of each player is to choose its location so as to maximize the number of clients it attracts.In this paper we study a variant of the Hotelling game where each client v has a tolerance interval, randomly distributed according to some density function f , and v gets served by the nearest among the players eligible for it, namely, those that fall within its interval. (If no such player exists, then v abstains.) It turns out that this modification significantly changes the behavior of the game and its states of equilibria. In particular, it may serve to explain why players sometimes prefer to "spread out," rather than to cluster together as dictated by the classical Hotelling game.We consider two variants of the game: symmetric games, where clients have the same tolerance range to their left and right, and asymmetric games, where the left and right ranges of each client are determined independently of each other. We characterize the Nash equilibria of the 2-player game. For n ≥ 3 players, we characterize a specific class of strategy profiles, referred to as canonical profiles, and show that these profiles are the only ones that may yield Nash equilibria in our game. Moreover, the canonical profile, if exists, is uniquely defined for every n and f . In the symmetric setting, we give simple conditions for the canonical profile to be a Nash equilibrium, and demonstrate their application for several distributions. In the asymmetric setting, the conditions for equilibria are more complex; still, we derive a full characterization for the Nash equilibria of the exponential distribution. Finally, we show that for some distributions the simple conditions given for the symmetric setting are sufficient also for a Nash equilibrium in the asymmetric setting.

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Location (Hotelling) Games and Applications

Cited by 17 publications

References 40 publications

Risk-balanced territory design optimization for a Micro finance institution

Risk-balanced territory design optimization for a Micro finance institution

From Recommendation Systems to Facility Location Games

Hotelling Games with Random Tolerance Intervals

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