## Journal Publications

Disclosing Information in
Strategic Experimentation, with K Drakopoulos

Submitted
for publication

[abstract]
[paper]

Multi-sourcing and
Miscoordination in Supply Chain Networks,
with D Fearing and A Tahbaz-Salehi

Submitted
for
publication

[abstract]
[paper]

Inventory Pooling under
Heavy-Tailed Demand, with M G Markakis

Submitted
for publication

[abstract]
[paper]

**Abstract:**

Risk pooling has been
extensively studied in the
operations management literature
as the basic driver behind
strategies such as
transshipment, manufacturing
flexibility, component
commonality, and drop-shipping.
This paper explores the benefits
of pooling in the context of
inventory management using the
canonical model first studied in
Eppen(1979). Specifically, we
consider a single-period
multi-location newsvendor model,
where different locations face
independent and identically
distributed demands and linear
holding and backorder costs. We
show that Eppen's celebrated
result, i.e., that the cost
savings from centralized
inventory management scale with
the square root of the number of
locations, depends critically on
the "light-tailed" nature of the
demand uncertainty. In
particular, we establish that
the relative benefits of risk
pooling for a class of
heavy-tailed demand
distributions (stable
distributions) scale as n^{(α-1)/α},
i.e., lower than √n predicted
for normally distributed
demands, where α ∈ (1,2] is a
parameter that captures the
shape of the distribution's
tail. Furthermore, we discuss
the implications of our findings
for the performance of
periodic-review policies in
multi-period inventory
management as well as for the
profits associated with
drop-shipping fulfillment
strategies. Paired with an
extensive simulation analysis,
these results highlight the
importance of taking into
account the shape of the tail of
the demand uncertainty when
considering implementing a
risk-pooling strategy.

Competing over
Networks, with A Ozdaglar and E
Yildiz

Submitted for publication

[abstract]
[paper]

**Abstract:**

Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers' preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms, which can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted marketing strategies and highlight their dependence on the underlying social network structure. Furthermore, we identify network structures for which the returns to targeting are maximized, and we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals. Finally, we provide a lower bound on the extent of asymmetry in these asymmetric equilibria and therefore shed light on the effect of the network structure to the outcome of marketing competition between firms.

**Abstract:**

This paper argues that in the presence of liquidation costs, portfolio diversification by financial institutions may be socially inefficient. We propose a stylized model in which individual banks have an incentive to diversify their risks. Yet, at the same time, diversification may increase the aggregate risk faced by the banks' depositors, creating a negative externality. The increase in systemic risk is due to the fact that even though diversification decreases the probability of each bank's failure, it may increase the probability of joint failures, which may be socially inefficient when the depositors are risk-averse. The presence of such externalities suggests that financial innovations that enable banks to engineer more diversified portfolios have non-trivial welfare implications.

Dynamics of
Information Exchange in Endogenous
Social Networks, with D Acemoglu
and A Ozdaglar

Theoretical Economics,
9(1): 41-97, January 2014

[abstract]
[paper]

**Abstract:**

We develop a model of
information exchange through
communication and investigate its
implications for information
aggregation in large societies. An
*underlying state*
determines payoffs from different
actions. Agents decide which
others to form a costly *communication
link* with incurring the
associated cost. After receiving a
*private signal* correlated
with the underlying state, they
exchange information over the
induced *communication network*
until taking an (irreversible)
action. We define *asymptotic
learning* as the fraction of
agents taking the correct action
converging to one as a society
grows large.

Under truthful communication, we
show that asymptotic learning
occurs if (and under some
additional conditions, also only
if) in the induced communication
network most agents are a short
distance away from *information
hubs*, which receive and
distribute a large amount of
information. Asymptotic learning
therefore requires information to
be aggregated in the hands of a
few agents. We also show that
while truthful communication may
not always be a best response, it
is an equilibrium when the
communication network induces
asymptotic learning. Moreover, we
contrast equilibrium behavior with
a socially optimal strategy
profile, i.e., a profile that
maximizes aggregate welfare. We
show that when the network induces
asymptotic learning, equilibrium
behavior leads to maximum
aggregate welfare, but this may
not be the case when asymptotic
learning does not occur.

We then provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results either if social cliques are not too large, in which case communication across cliques is encouraged, or if there exist very large cliques that act as information hubs.

Optimal Pricing in
Networks with Externalities, with
O Candogan and A Ozdaglar

Appeared
as an Extended Abstract at WINE
2010

Operations
Research, 60(4): 883-905,
July-August 2012

[abstract]
[paper]

**Abstract:**

We study the optimal pricing
strategies of a monopolist selling
a divisible good (service) to
consumers that are embedded in a
social network. A key feature of
our model is that consumers
experience a (positive) *local
network effect*. In
particular, each consumer's usage
level depends directly on the
usage of her *neighbors*
in the social network structure.
Thus, the monopolist's optimal
pricing strategy may involve
offering discounts to certain
agents, who have a *central*
position in the underlying
network.

Our results can be summarized as
follows. First, we consider a
setting where the monopolist can
offer individualized prices and
derive an explicit
characterization of the optimal
price for each consumer as a
function of her network position.
In particular, we show that it is
optimal for the monopolist to
charge each agent a price that is
proportional to her *Bonacich
centrality* in the social
network. In the second part of the
paper, we discuss the optimal
strategy of a monopolist that can
only choose a single uniform price
for the good and derive an
algorithm polynomial in the number
of agents to compute such a price.

Thirdly, we assume that the monopolist can offer the good in two prices, full and discounted, and study the problem of determining which set of consumers should be given the discount. We show that the problem is NP-hard, however we provide an explicit characterization of the set of agents that should be offered the discounted price. Next, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit. Finally, we highlight the value of network information by comparing the profits of a monopolist that does not take into account the network effects when choosing her pricing policy to those of a monopolist that uses this information optimally.

Experimentation,
Patents, and Innovation, with D
Acemoglu and A Ozdaglar

*American Economic Journal:
Microeconomics, 3(1): 37-77,
February 2011*

[abstract]
[paper]

**Abstract:**

This paper studies a simple model of experimentation and innovation. Our analysis suggests that patents improve the allocation of resources by encouraging rapid experimentation and efficient ex post transfer of knowledge. Each firm receives a signal on the success probability of a project and decides when to experiment. Successes can be copied. First, we assume that signal qualities are the same. Symmetric equilibria involve delayed and staggered experimentation, whereas the optimal allocation never involves delays and may involve simultaneous experimentation. Appropriately designed patents implement the optimal allocation. Finally, we discuss the case when signals differ and are private information.

## Conference Papers

Cournot Competition in Networked Markets, with S Ehsani and R Ilkilic, EC 2014Optimal Pricing in
the Presence of Local Network
Effects, with O Candogan and A
Ozdaglar,* WINE 2010*

Forming Information
Networks, * *with D
Acemoglu and A Ozdaglar,* **Allerton,
2010
*

Communication and
Learning in Social Networks:
Partial Results, * *with
D Acemoglu and A Ozdaglar,* **Allerton,
2009
*

Competition with
Atomic Users, with A Ozdaglar,*
Asilomar, 2007*

Partial Results on
Capacity Competition, with D
Acemoglu and A Ozdaglar,*
Allerton, 2006 *