A game theoretic approach to optimize multi-stakeholder utilities for land acquisition negotiations with informality
Rounaq Basu and
Socio-Economic Planning Sciences, 2020, vol. 69, issue C
To combat the critical stresses of rising urbanization, the government acquires land from private owners using the power of eminent domain. This land assembly causes negative externalities such as increasing social tension and injustice that may impose a long-term threat to stability and sustainable development. Therefore, there is a need to make the land acquisition process more transparent and just. Considering the unique Indian context where informality has a strong presence, we propose an approach based on game theory that models the bargain through a three-stage Nash equilibrium game. Four agents – the government, the private developer, the landowner, and the free rider – are considered. We provide conditional solutions for the generalizable Case and proceed to model different stakeholder behavior patterns through two utility functional forms – linear and exponential. In the linear case, we find that the free rider obtains half of the revenue of the project, whereas the landowner gains between one-fourth and one-half of the revenue. Thus, we highlight the undeniably crucial role free riders play in land acquisition negotiations. However, closed form solutions cannot be obtained for the exponential form, due to which we use simulations to demonstrate a solution procedure. We conclude by stating that the proposed model can be useful in formulating future land policies in a sustainable and inclusive manner, with optimal utility derivations for all concerned stakeholders. Our model can also be extended to other spatial contexts where informality features heavily in the land market, especially in the Global South.
Keywords: Land acquisition; Land assembly; Eminent domain; Game theory; Nash equilibrium; Informal land markets (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:69:y:2020:i:c:s0038012118301939
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