Mohammad Akbarpour

I am an Assistant Professor of Economics at Stanford Graduate School of Business.

Stanford University
Graduate School of Business
655 Knight way, E308
Stanford, CA, 94305.

"Somewhere, something incredible is waiting to be known." - Carl Sagan

Curriculum Vitae

Research Interests:
I work on market design, networked markets, and how design and networks interact with inequality. I'm particularly interested in understanding how the kidney exchange markets, school choice systems, labor markets, and over-the-counter financial markets evolve over time, and to improve their designs.

Research Papers

Thickness and Information in Dynamic Matching Markets (with Shengwu Li and Shayan Oveis Gharan)
- A preliminary version of this paper is the winner of the Sean Buckly memorial award, best 2nd year paper award, Stanford University Department of Economics and Stanford Institute for Economic Policy Research (2013).
- A preliminary version of this paper (with title: Dynamic Matching Market Design) was accepted for presentation in 15th ACM conference on Economics and Computations (EC'14).

We introduce a simple model of dynamic matching in networked markets, where agents arrive and depart stochastically, and the composition of the trade network depends endogenously on the matching algorithm. We show that if the planner can identify agents who are about to depart, then waiting to thicken the market is highly valuable, and if the planner cannot identify such agents, then matching agents greedily is close to optimal. We characterize the optimal waiting time (in a restricted class of mechanisms) as a function of waiting costs and network sparsity. The planner's decision problem in our model involves a combinatorially complex state space. However, we show that simple local algorithms that choose the right time to match agents, but do not exploit the global network structure, can perform close to complex optimal algorithms. Finally, we consider a setting where agents have private information about their departure times, and design a continuous-time dynamic mechanism to elicit this information.

Approximate Random Allocation Mechanisms (with Afshin Nikzad)

We extend the scope of random allocation mechanisms, in which the mechanism first identifies a feasible "expected allocation" and then implements it by randomizing over nearby feasible integer allocations. Previous literature had shown that the cases in which this is possible are sharply limited. We show that if some of the feasibility constraints can be treated as goals rather than hard constraints then, subject to weak conditions that we identify, any expected allocation that satisfies all the constraints and goals can be implemented by randomizing among nearby integer allocations that satisfy all the hard constraints exactly and the goals at least approximately. We show that by adding ex post utility goals to random serial dictatorship, we can construct a strategy-proof mechanism with the same ex ante utility that is nearly ex post fair.

Diffusion in Networks and the Unexpected Virtue of Burstiness (with Matthew O. Jackson)

Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in “bursts”, active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns – some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents’ activity times does not involve any any Poisson behavior.

A New Interpretation of Dictatorship with Applications in Social Choice Theory (with Sam Nariman)

We say that a social choice function is sensitive to collective change if whenever all inputs change, the outcome necessarily changes. Then, we prove that a social choice function is sensitive to collective change if and only if it is dictatorial. This provides a new interpretation of what dictatorship essentially means. We provide an intuitive, geometric proof for a special case of the theorem, which reveals the geometric meaning of dictatorship. In addition, the theorem provides a unified framework to prove other dictatorship results in theoretical social choice; to illustrate this, we show how the Arrow and Gibbard-Satterthwaite theorems can be deduced from this theorem.
Research in Progress

Financing Transplants' Costs of the Poor: A Dynamic Model of International Kidney Exchange (with Afshin Nikzad and Alvin Roth)

A Regulated Market for Kidneys: Theoretical and Empirical Analysis of the Iranian System of Paid Donation (with Farshad Fatemi and Negar Matoorian)


Massive Open Online Courses

Game Theory (Volunteer teacher in Khan Academy (Farsi), 2012, 27 videos, 30,000+ views)

Principles of Macroeconomics (Volunteer teacher in Khan Academy (Farsi), 2012, 20 videos, 20,000+ views)

Mechanical Physics (Volunteer teacher in Khan Academy (Farsi), 2012, 110 videos, 80,000+ views)

Introductory Calculus (Volunteer teacher in Khan Academy (Farsi), 2012, 80 videos, 50,000+ views)

Sample Videos (all in Farsi): Newton's 3rd Law -- How airbags save your life? -- Greece Financial Crisis -- Most viewed video: Circular Flow of Income (5000+ views)