# A network ridesharing experiment with sequential choice of transportation mode

- 92 Downloads

## Abstract

Within the last decade, there has been a dramatic bloom in ridesharing businesses along with the emergence of new enabling technologies. A central issue in ridesharing, which is also important in the general domain of cost-sharing in economics and computer science, is that the sharing of cost implies positive externalities and hence coordination problems for the network users. We investigate these problems experimentally in the present study. In particular, we focus on how sequential observability of transportation mode choices can be a powerful facilitator of coordination in ridesharing. Our study abstracts the essential issues of coordination in ridesharing into a directed network game with experimentally testable predictions. In line with the theoretical analysis, our experimental evidence shows that even a limited extent of sequential choice observability might lead to efficient coordination. However, convergence to efficiency is slower with more limited observability, resulting in a significant increase in travel cost.

## Keywords

Ridesharing Traffic networks Sequential choice Experiment## Notes

### Acknowledgements

This research was supported by NSF Grant SES-1418923 awarded to the University of Nevada, Las Vegas and University of Arizona.

## References

- Acemoglu, D., Makhdoumi, A., Malekian, A., & Ozdagar, A. (2016). Informational Braess’ Paradox: The effect of information on traffic congestion. arXiv:1601.02039v1.Google Scholar
- Agatz, N. A. H., Erera, A. L., Savelsbergh, M. W. P., & Wang, X. (2012). Optimization for dynamic ride-sharing: A review.
*European Journal of Operational Research,**223,*295–303.CrossRefGoogle Scholar - Anshelevitch, E., Dasgupta, A., Kleinberg, J., Tardos, É., Wexler, T., & Roughgarden, T. (2008). The price of stability for network design with fair cost allocation.
*SIAM Journal of Computing,**38,*1602–1623.CrossRefGoogle Scholar - Arieli, I., & Aumann, R. J. (2015). The logic of backward induction.
*Journal of Economic Theory,**159,*443–464.CrossRefGoogle Scholar - Berbeglia, G., Cordeau, J.-F., & Laporte, G. (2010). Dynamic pickup and delivery problems.
*European Journal of Operational Research,**202,*8–15.CrossRefGoogle Scholar - Charness, G., Ferl, F., Meléndez-Jiménez, M. A., & Sutter, M. (2014). Experimental games on networks: Underpinnings of behavior and equilibrium selection.
*Econometrica,**82,*1615–1670.CrossRefGoogle Scholar - Chen, H.-L., & Chen, Y. (2011). The potential of social identity for equilibrium selection.
*American Economic Review,**101,*2562–2589.CrossRefGoogle Scholar - Chen, H.-L., Roughgarden, T., & Valiant, G. (2010). Designing network protocols for good equilibria.
*SIAM Journal of Computing,**39,*1799–1832.CrossRefGoogle Scholar - Chow, Y. S., Moriguti, S., Robbins, H., & Samuels, S. M. (1964). Optimal selection based on relative rank (the “secretary problem”).
*Israel Journal of Mathematics,**2,*81–90.CrossRefGoogle Scholar - Crawford, V. P. (1995). Adaptive dynamics in coordination games.
*Econometrica,**63,*103–144.CrossRefGoogle Scholar - Erev, I., & Rapoport, A. (1998). Coordination, “magic”, and reinforcement learning in market entry games.
*Games and Economic Behavior,**23,*313–325.CrossRefGoogle Scholar - Ferguson, T. S. (1989). Who solved the secretary problem?
*Statistical Science,**4,*282–289.CrossRefGoogle Scholar - Furuhata, M., Dessouky, M., Ordonez, F., Brunet, M., Wang, X., & Koenig, S. (2013). Ridesharing: The state-of-the-art and future directions.
*Transportation Research Part B: Methodological,**57,*28–46.CrossRefGoogle Scholar - Gisches, E., & Rapoport, A. (2012). Degrading network capacity may improve performance: Information effects in the Braess paradox.
*Theory and Decision,**73,*267–293.CrossRefGoogle Scholar - Goeree, J., & Holt, C. (2015). An experimental study of costly coordination.
*Games and Economic Behavior,**51,*349–364.CrossRefGoogle Scholar - Hoefer, M. (2013). Strategic cooperation in cost sharing games.
*International Journal of Game Theory,**42,*29–53.CrossRefGoogle Scholar - Jain, K., & Mahdian, M. (2007). Cost sharing. In N. Nisan, T. Roughgarden, É. Tardos, & V. Vazirani (Eds.),
*Algorithmic game theory*(pp. 385–410). Cambridge: Cambridge University Press.CrossRefGoogle Scholar - Liu, C., Mak, V., & Rapoport, A. (2015). Cost-sharing in directed networks: Experimental study of equilibrium choice and system dynamics.
*Journal of Operations Management,**39–40,*31–47.CrossRefGoogle Scholar - Mak, V., Zwick, R., Rao, A. R., & Pattaratanakun, J. A. (2015). “Pay what you want” as threshold public good provision.
*Organizational Behavior and Human Decision Processes,**127,*30–43.CrossRefGoogle Scholar - Morgan, J., Orzen, H., & Sefton, M. (2009). Network architecture and traffic flow: Experiments on the Pigou-Knight-Downs and Braess paradoxes.
*Games and Economic Behavior,**66,*348–372.CrossRefGoogle Scholar - Moulin, H. (2002). The proportional random allocation of indivisible units.
*Social Choice and Welfare,**19,*381–413.CrossRefGoogle Scholar - Rapoport, A., Gisches, E., & Mak, V. (2014). Distributed decisions in networks: Laboratory study of routing splittable flow.
*Production and Operations Management,**23,*314–331.CrossRefGoogle Scholar - Rapoport, A., Kugler, T., Dugar, S., & Gishces, E. (2009). Choice of routes in congested traffic networks: Experimental tests of the Braess paradox.
*Games and Economic Behavior,**65,*538–571.CrossRefGoogle Scholar - Rapoport, A. & Mak, V. (in press). Strategic interaction in transportation networks. In K. Donohue, E. Katok, & S. Leider (Eds.),
*Handbook of behavioral operations*, New York: Wiley.Google Scholar - Rapoport, A., Mak, V., & Zwick, R. (2006). Navigating congested networks with variable demand: Experimental evidence.
*Journal of Economic Psychology,**27,*648–666.CrossRefGoogle Scholar - Rubin, H., & Samuels, S. M. (1977). The finite-memory secretary problem.
*The Annals of Probability,**5,*627–635.CrossRefGoogle Scholar - Samuels, S. M. (1991). Secretary problems. In P. K. Sen (Ed.),
*Handbook of sequential analysis*(pp. 381–405). Boston: Marcel Dekker.Google Scholar - Schelling, T. C. (1960).
*The strategy of conflict*. Cambridge: Harvard University Press.Google Scholar - Seale, D. A., & Rapoport, A. (2000). Elicitation of strategy profiles in large group coordination games.
*Experimental Economics,**3,*153–179.CrossRefGoogle Scholar - Selten, R., Chmura, T., Pitz, T., Kube, S., & Schreckenberg, M. (2007). Commuters route choice behavior.
*Games and Economic Behavior,**58,*394–406.CrossRefGoogle Scholar - Shubik, M. (1982).
*Game theory in the social sciences*(Vol. 1). Cambridge: MIT Press.Google Scholar - Syrgkanis, V. (2010). The complexity of equilibria in cost sharing games.
*Internet and Network Economics*, pp. 366–377.Google Scholar - Van Huyck, J. B., Battalio, R. C., & Beil, R. O. (1990). Tacit coordination games, strategic uncertainty, and coordination failure.
*American Economic Review,**80,*234–248.Google Scholar - Van Huyck, J. B., Battalio, R. C., & Beil, R. O. (1991). Strategic uncertainty, equilibrium selection, and coordination failure in average opinion games.
*Quarterly Journal of Economics,**91,*885–910.CrossRefGoogle Scholar - Young, H. P. (1985). Producer incentives in cost allocation.
*Econometrica,**53,*757–765.CrossRefGoogle Scholar - Young, H. P. (1988). Cost allocation, demand revelation, and core implementation.
*Mathematical Social Sciences,**36,*213–228.CrossRefGoogle Scholar - Young, H. P. (1994). Cost allocation. In R. Aumann & S. Hart (Eds.),
*Handbook of game theory with economic applications*(Vol. 2, Ch. 34, pp. 1194–1235).Google Scholar