ECONOMIC ANALYSIS GROUP 

DISCUSSION PAPER

 

Investment Incentives and Market Power: 

An Experimental Analysis 

by 

Dean V. Williamson,1 Céline Jullien,2 Lynne Kiesling,3

Carine Staropoli4

 

EAG 06-5 January 2006

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We thank Dominique Finon, participants in the 2004 meetings of the International Society for New Institutional 

Economics, and seminar participants at the Federal Reserve Bank of Dallas, the Interdisciplinary Center for 

Economic Science at George Mason University, Purdue University, and West Virginia University for helpful 

comments and suggestions relating to an earlier incarnation of this paper. The usual disclaimers apply with one 

exception: The views expressed in this paper do not reflect those of the United States Department of Justice. 

1

 Contact Author. United States Department of Justice: dean.williamson@usdoj.gov. The views expressed in this 

paper do not reflect those of the United States Department of Justice.

2

 Université de Grenoble: celine.jullien@ensgi.inpg.fr 

3

 International Foundation for Research in Experimental Economics (IFREE) and Department of Economics, 

Northwestern University: lkiesling@northwestern.edu, who gratefully acknowledges funding from the Searle Fund 

for Policy Research at Northwestern University and IFREE for this research. 4

 Université Panthéon-Sorbonne: carine.staropoli@univ-paris1.fr 
1

Abstract 

We examine investment incentives and market power in an experimental market. We 

characterize market power as the strategic interdependence of subjects’ investment decisions and 

output decisions. The market is designed so that investment and output decisions can be jointly 

characterized as strategies within a game. A Nash-Cournot equilibrium of the game provides a 

way of characterizing how investment incentives and market power interact. Subjects could 

invest in two different production technologies and could produce output to serve as many as two 

different demand conditions. The technologies were analogous to “baseload” capacity and 

“peaking” capacity in wholesale electricity markets. The Nash-Cournot benchmark constituted a 

good indicator of subjects’ output decisions in that output cycled around the Cournot benchmark. 

Thus, on average, consumers extracted the surplus available to them in the equilibrium. While 

we do not observe Edgeworth Cycles in prices or outputs, we do see them in the producer surplus 

series. Producers dissipated some of the surplus they could have extracted in the equilibrium by 

overinvesting in peaking capacity and underinvesting in baseload capacity. Inefficient 

investment diminished total system efficiency, but producers’ investments in total production 

capacity tracked the Nash-Cournot benchmark. In contrast, monopoly explanations such as 

collusion do not characterize the data. 

keywords: capacity investment, Cournot, supply function equilibrium, Edgeworth Cycles, market 

power, electricity markets, investment incentives 

JEL codes: C92, D43, C72 
1. Introduction 

In 1993 Charles Plott observed that “Designer markets are becoming a reality.” (Plott 1994, pg. 

3) Since then, market design has become big business, so much so that the topic has migrated, 

under various guises, from the theorist’s notepad, to the policymakers’ forum, to the front page 

of any newspaper. Casual consumers of news may, for example, know something about the 

market-based “cap-and-trade” mechanisms proponents of the Kyoto Protocol on global warming 

have engineered. They may also know something about the auction mechanisms government 

agencies have used for allocating rights to radio spectra or about schemes municipalities have 

been implementing to mitigate traffic congestion. Yet others may have experience with the 

efforts of regulators to create wholesale electricity markets. All of these applications – pollution 

abatement, spectrum auctions, network congestion, and wholesale electricity – constitute just a 

few of the allocation problems within the designer’s purview. 

Part of the appeal of actively designing markets is that it may allow parties to (1) achieve 

superior allocative efficiency and to (2) achieve it in environments that are hostile to the 

spontaneous emergence of markets. Banks, Ledyard and Porter (1989) discuss environments in 

which exchange mediated in institutions that look a lot like traditional markets may be difficult 

to operationalize. They observe that even when one can organize traditional market institutions, 

the allocative efficiency of markets may not dominate that of centralized, administrative means 

of organizing exchange. But that just raises the ante for designers. The authors’ principal point 

is that scope may yet remain for designing (possibly non-traditional) market rules to improve the 

performance of decentralized exchange mechanisms. 

In designing rules governing exchange in wholesale electricity markets, policymakers have 

endeavored to design rules that would frustrate the exercise of market power (Wolak and Patrick 

2001). Even so, one of the principal motivations for “restructuring” wholesale electricity 

markets was explicitly dynamic: to give energy firms incentives to invest over time in new, 

cleaner, more efficient generation and transmission capacity, all in an effort to move electrons 

and get the lights on at lower cost (Roques, Newbery and Nuttall 2005; Blumstein, Friedman and 

Green 2002). More efficient producers would be better situated to capture gains from trade in 

day-to-day exchange mediated in restructured markets; those gains would create opportunities 

and incentives to invest. Less efficient capacity would prove unprofitable to maintain; firms 

would substitute more efficient capacity for less efficient capacity. In the process both producers 

and consumers would gain from increased efficiency. 

Electricity market restructuring did induce investment in new generation capacity, but the dayto-day experiences in restructured markets sometimes proved to be problematic or, at least, 

politically incorrect. In some applications allocative efficiency may be decomposed into static 

and dynamic dimensions; market rules (and changes to them) can have both static and dynamic 

effects. One static consideration is market power, in which the primary concern is whether the 

market rules enable participants to raise short-run prices by reducing output. Among other 

things, trade in restructured markets exhibited “price spikes,” seen most notably in California in 

2000-2001. These price spikes have been attributed to the ability of generators to exercise 

market power (Borenstein, Bushnell, and Wolak 2002; Joskow and Kahn 2002). 
2

It is not obvious that price spikes are pathological per se, because price increases grounded in 

true scarcity provide valuable investment signals, but they have attracted negative attention. 

Even if one can attribute price spikes to the exercise of market power, there may be interactions 

between investment behaviors and such manifestations of market power. How such interactions 

can inform the design of markets remains poorly understood. On one hand, the motivation to 

restructure markets rests on dynamic considerations (investment over time), but the seemingly 

pathological results experienced in restructured markets highlights static considerations (market 

power). 

The research presented in this paper shifts focus to broader theoretical questions about 

relationships between investment incentives and market processes and presents some evidence 

on these relationships in experimental markets. 

The paper presents a model that enables characterization of interactions between investment and 

market power. Do investment and market outcomes correspond to theoretical benchmarks? Do 

they systematically deviate from theoretical benchmarks? Do market participants tend to invest 

beyond theoretical benchmarks? Do they systematically underinvest? Are certain market 

designs more conducive to investment than others? Ultimately, can we generate discrete results 

about the interactions of investment incentives and market power that can inform market design? 

We characterize investment incentives and market power in a simple model, and we use that 

model to guide the design of an experiment. One great advantage of the model is that it provides 

a way of operationalizing the concept of market power. It allows us to characterize market 

power as the strategic interdependence of parties’ investment decisions and output decisions. In 

turn, the appeal to strategic interdependence allows us to characterize competition among parties 

who possess market power as a game. Equilibria of the game constitute predictions of how 

investment behaviors and market processes interact. 

The model borrows two salient features of electricity markets: parties can invest in more than 

one production technology, and they may compete in markets featuring different demand 

conditions. The production technologies are analogous to “baseload” and “peaking” capacity in 

energy markets, and the demand conditions are analogous to “off-peak” demands and “peak” 

demands in those same markets. The structure of the game provides a simple way of 

characterizing parties’ investments in more than one production technology. A striking feature 

of the experimental design is that it allows us to characterize those investments without having to 

appeal to other, real-world features of energy technologies such as “ramp rates,” startup costs, 

and shutdown costs – features that might distract analysis from the more fundamental 

considerations. 

We can characterize behaviors in the experimental markets as Cournot, and we craft a Cournot 

benchmark and other theoretical benchmarks to characterize how well experimental subjects 

invest. The principal results are that the Cournot benchmark constitutes a good indicator of 

subjects’ output decisions and, accordingly, that the system generates Cournot-consistent 

consumer surplus. Subjects extract less than the Cournot-consistent producer surplus, because 

they tend to overinvest in peaking capacity and underinvest in baseload capacity. The data do 

not support monopoly explanations such as collusion for subjects’ behaviors. The Cournot 
3

benchmark also constitutes a better predictor of behaviors than the benchmark corresponding to 

socially optimal investment and outputs. 

The existing experimental framework allows us to generate a number of important results, but it 

also constitutes an expandable platform for analysis of more complex questions. It does not yet 

accommodate questions about investment over time or questions about investment in new 

technologies, although it can be easily extended to do so. Indeed, extending the framework will 

be the object of immediate follow-on research. In the framework applied here, subjects 

participated in a sequence of games, and we can interpret each game as a stage game of a larger, 

repeated game. The technologies remained fixed across all of the stage games, and each stage 

game was static in that subjects’ investment decisions did not roll over into succeeding stages. 

As it is, the existing framework allows us to identify important dynamic phenomena such as 

“sawtooth patterns” or “Edgeworth Cycles” (Maskin and Tirole 1988) in the data. 

The remainder of the paper proceeds in five parts. The first part reviews literature involving 

applications of Nash-Cournot hypotheses. The second part presents a model we used to inform 

the design of the experiment, the third part features the experimental design, and the fourth part 

presents the results of the experiment. The final part concludes. 

2. Related Literature

The research presented here relates to three broad lines of research: (1) empirical research on 

oligopolistic competition in wholesale electricity markets, (2) experimental research on 

oligopolistic competition in quantity-setting environments, and (3) “dynamic oligopoly” more 

generally. The first illuminates the advantages and limits of Cournot hypotheses in evaluating 

electricity markets. The second provides some basis for evaluating Cournot hypotheses 

themselves and extends analysis to competition that joins quantity-setting behaviors with pricesetting behaviors. The third body of literature points up dynamic considerations that hypotheses 

about behaviors in a static environment, including Cournot hypotheses, cannot accommodate. 

Research on the performance of wholesale electricity markets suggest that, both as a matter of 

theory and practice, Nash-Cournot hypotheses constitute conservative benchmarks. The 

implication is powerful: Posing Nash-Cournot behaviors allows analysts to characterize worstcase scenarios. Worst-case scenarios constitute conservative forecasts of market performance. 

Thus, policymakers and industry analysts can use these Cournot-consistent forecasts to 

benchmark prospective changes in the allocation of industry production capacity. Such changes 

might attend discrete events like mergers, new entry, or market restructuring. 

Borenstein and Bushnell (1999) explicitly appealed to such reasoning when forecasting the 

performance California’s restructured electricity market (pp. 289-291). They were interested in 

evaluating one of the principal features of the “restructuring”: the divestiture of electricity 

generation capacity from vertically-integrated utilities to vertically-separated generating firms. 

They used measures of firms’ marginal costs and imposed a demand function featuring constant 

elasticity. Their simulation of market performance amounted to imposing different elasticities of 

demand and calculating the Cournot-consistent outputs those elasticities implied. 
4

Posing Cournot behaviors allowed them to benchmark market performance that would succeed 

divestiture, and, presciently they suggested that the availability of “hydro[electric] power in the 

Pacific northwest and California plays a large role in determine the extent and severity of market 

power.” (pg. 299) By the summer of 2000, drought had denied California much of the 

hydroelectric generation upon which it had depended, contributing to “price spikes” experienced 

in wholesale electricity markets. 

Borenstein and Bushnell observe that empirical research on electricity markets has appealed to 

different models: the “supply function equilibrium” approach (SFE) innovated by Klemperer and 

Meyer (1989), Cournot models, and auction models. (pp. 288-289) Klemperer and Meyer 

observe that Cournot constitutes a special, worst case of SFE, a fact that Borenstein and Bushnell 

use to substantiate their appeal to Cournot. 

The great advantage of Cournot, of course, it its tractability in understanding strategic 

interdependence of producers’ output decisions. SFE is more general because it goes some way 

toward accounting for price-setting behavior; the concept of bidding entire “offer curves” more 

accurately reflects exchange in markets such as wholesale electricity markets. In contrast, 

Cournot relies implicitly on an unmodeled market institution that allocates output to parties on 

the demand side. Yet, while SFE is more general, there is a familiar tradeoff: Cournot “allows 

much more detailed modeling and determinacy in solutions than the supply curve bidding 

approach.” (Borenstein and Bushnell 1999, pg. 290) Borenstein and Bushnell go on to observe 

that Cournot constitutes a “worst-case (static equilibrium) scenario” under SFE – the suggestion 

being that it constitutes a conservative benchmark of performance. 

Other research has exploited the more general concept of SFE. Green and Newbery (1992) 

extended SFE to accommodate supply constraints so that they could apply SFE to electricity 

markets. Their approach is interesting, because supply constraints constitute an important 

condition in electricity markets, but their SFE model leaves open the prospect of extending the 

static analysis of markets to an analysis that is dynamic in that it accounts for the endogeneity of 

those constraints. 

While SFE provides a theoretical basis for the proposition that Cournot constitutes a 

conservative benchmark, the question of whether or not Cournot is conservative is ultimately 

empirical. It turns out there is some empirical evidence consistent with the proposition. 

Wolfram (1999) observes that outcomes in the British market were not as bad as SFE (and thus, 

Cournot) suggest they could have been. Specifically, prices were not as high as SFE and 

Cournot simulations suggest they could have been. An affirmative interpretation of her results is 

consistent with the Borenstein and Bushnell approach: Cournot may not exactly characterize 

behaviors but, as a matter of theory and practice, it constitutes a conservative bound. 

The proposition that Cournot constitutes a conservative benchmark for evaluating market 

performance is empowering. It suggests that if posing the Nash-Cournot hypothesis allows 

researchers to indicate that markets are performing well, then they need not agonize over the 

prospect of operationalizing more ambitious hypotheses. Even so, one might hope that one could 

appeal to other empirical research to evaluate the proposition. Wolfram herself suggested that 
5

“It is unclear to what extent one can draw implications from the experience in the British 

[wholesale electricity market] for electricity industry restructurings elsewhere.” (pg. 822) 

Wolfram observed that it is not obvious why prices observed in the British were lower than 

benchmark models predicted and went on to suggest that idiosyncratic features of the British 

market rather than systematic features of behaviors in markets could account for the results. 

The question of whether or not Cournot poses a conservative benchmark can be tested using the 

experimental method. While no experimental research has explicitly taken up this question, 

there is abundant research on competition in quantity-setting environments. Much of this 

research also joins quantity-setting competition with price-setting competition. The point of 

departure for much of this experimental research is the hypothesis of Kreps and Scheinkman 

(1983) according to which capacity investment can yield Cournot-consistent outputs in 

environments explicitly featuring price-setting competition. 

Cournot hypotheses alone are silent on the question of price formation. Once we explicitly 

account for price formation, the proposition that Cournot constitutes a conservative benchmark 

might prove to be less robust. Kreps and Scheinkman (KS) provide a theoretical foundation for 

linking price formation with investment in production capacity, and their results provide a 

rationale for appealing to Cournot. Yet other theoretical research suggests how the appeal to 

capacity constraints can yield behaviors that are richer than a Cournot-consistent hypothesis 

would predict. Edgeworth, for example, suggested that in the context of repeated interactions – 

what Maskin and Tirole (1988) call “dynamic oligopoly” – that capacity constraints can induce 

competitors to generate outputs and prices that cycle over time. (Edgeworth 1925, pp. 118-121) 

These cycles are the “Edgeworth Cycles” of Maskin and Tirole. 

The KS hypothesis has inspired much follow-on research, both theoretical and experimental, on 

whether or not Cournot-consistent outcomes do obtain in a wide range of environments. 

Theoreticians have pointed out that the KS hypothesis is sensitive to assumptions about the 

structure of residual demand (e.g. Davidson and Deneckere 1986). Specifically, KS depends on 

“efficient rationing” – demand is assigned to the lowest-priced output. Introducing uncertainty 

in demand to the KS formulation with symmetric firms can also lead to multiple equilibria, no 

one of which may be symmetric (e.g., Reynolds and Wilson 2000). Yet other researchers have 

generalized the KS formulation by admitting the prospect that capacity may not be strictly 

constrained at the margin (e.g., Boccard and Wauthy 2000) or that firms may adjust capacity in 

the context of repeated oligopoly (Güth and Güth 2001). Either way, making capacity more 

elastic introduces more scope for symmetric Cournot outputs to obtain in equilibrium (Güth 

1995). 

What do experiments have to say? In general, the results are consistent with the proposition that 

Cournot constitutes a conservative benchmark. In environments featuring efficient-rationing, 

Rassenti, Reynolds, Smith, and Szidarovsky (2000) observed average outputs that were Cournotconsistent or Cournot-superior. In an environment in which subjects explicitly chose both 

capacities and prices, Cournot-consistent or Cournot-superior capacities obtained on average 

(Muren 2000). In both of these environments, individual behaviors exhibited much 

heterogeneity. Anderhub, Güth, Kamecke and Normann (2003) also observe Cournot-consistent 
6

capacities in an environment featuring repeated oligopoly and staged investment. In their 

environment, subjects also chose both capacities and prices. 

Before giving the Cournot benchmark a complete pass, we should note at least one other set of 

complementary results. In related work, Rassenti and Wilson (2004) examine experimentally the 

role of the “dominant firm” and “competitive fringe firms” in a quantity-setting environment. 

Their point of reference is the Borenstein and Bushnell (1999) hypothesis. Borenstein and 

Bushnell pose a Cournot oligopoly of dominant firms and a fringe of price-taking firms. Thus, 

they maintain not so much that Cournot poses a conservative benchmark but that a dominant 

Cournot oligopoly with a price-taking fringe constitutes a conservative benchmark. Rassenti and 

Wilson simplify analysis by posing a single dominant firm rather than a group of dominant firms 

and find that the “dominant firm model” itself does not constitute a conservative benchmark in 

some respects.. Capacity is exogenous in their environment, but they do examine an 

environment in which all firms post prices. They also examine an environment in which subjects 

submit “offer curves” in a manner consistent with the supply function equilibrium approach of 

Klemperer and Meyer. They find that outputs often exceed the output implied by the dominant 

firm model, but they also find that prices often exceeded predicted prices. In the environment 

they examine, firms on the fringe do not behave as price-takers. 

A richer treatment would incorporate (1) endogenous capacity (2) explicit price setting and (3) 

active demand such as that encountered in a double auction. One only needs endogenous 

capacity and explicit price setting to craft tests of KS-type hypotheses, but no one experiment 

includes all three, including the environment we examine. Indeed, our environment only features 

endogenous capacity. That alone is not novel. What is novel is the extension to a richer set of 

production technologies. Our immediate purpose was not to craft tests of KS-type hypotheses 

but to generate results in a simpler environment that might yet scale up to a richer environment. 

As with other experiments, individual behaviors in our environment also exhibit much 

heterogeneity. We find that Cournot-consistent outputs obtain on average. 

3. The Model 

We consider an environment in which agents can invest in two types of production capacity and 

in a risk-free security. The two types of capacity are analogous to “baseload” generation 

capacity and “marginal” (or “peaking”) generation capacity in energy markets. Agents can use 

both types of capacity to produce output in a sequence of two markets. One market is 

characterized by “low” demand, and the other is characterized by “high” demand. We label 

these markets “Market L” and “Market H,” respectively. 

Agents are endowed with investment resources. They must decide how much to invest in the 

risk-free security, in baseload capacity, and in marginal (“peaking”) capacity before participating 

in markets L and H. They also must decide how much capacity of each type to dispatch in each 

of the two markets. 
7

The distinguishing feature of baseload capacity is that it is more expensive to install than 

marginal capacity, but it operates at lower marginal costs. We indicate the fixed costs of 

building capacity and the marginal costs of production as follows: 

 Fixed Cost Marginal Cost 

Baseload Capacity FB per unit of capacity CB per unit of capacity 

Marginal Capacity FM per unit of capacity CM per unit of capacity 

We assume FM < FB and CM > CB . 

Competition can be characterized as Cournot, and it is easy to characterize equilibria in which 

agents invest in some combination of baseload capacity, marginal capacity, and the risk-free 

security. We are principally interested in equilibria in which agents invest in both types of 

capacity and in the risk-free asset. 

We characterize competition by means of a static, constrained optimization problem. For any 

one agent, we let 

λ R = investment in risk-free security 

λ B = investment in baseload capacity 

λ M = investment in marginal capacity 

r = the risk-free rate of return 

Λ = agent’s endowment of investment resources. 

σ LB = volume of baseload capacity dispatched in period L 

σ HB = volume of baseload capacity dispatched in period H 

σ LM = volume of marginal capacity dispatched in period L 

σ HM = volume of marginal capacity dispatched in period H 

The environment features N agents, each of whom faces residual demand functions PL and PH

where 

( ) PL σ LB +σ LM + Σ−L = demand in the low-demand state where Σ−L indicates the capacity 

dispatched by all other N-1 agents. 

( ) PH σ HB +σ HM + Σ−H = demand in the low-demand state where Σ−H indicates the 

capacity dispatched by all other N-1 agents. 

The agent’s choice variables are λ R λ B λ M σ LB σ HB σ LM , , , , , and σ HM . We indicate Lagrange 

multipliers as R B M LB HB LM HM LB HB LM μ, μ , μ , μ , μ , μ , μ , μ ,γ ,γ ,γ and HM γ . 
8

The corresponding maximization program is 

( )

( )

R B B M M

H HB HM H HB HM B HB M HM

L LB LM L LB LM B LB M LM

r F F

P C C

Max P C C

λ λ λ

σ σ σ σ σ σ

π σ σ σ σ σ σ

+ − −

+ + + Σ + − −

= + + Σ + − −

−

−

( )

( )

1B 1M 2B 2M

R B M σ , σ , σ , σ

λ , λ , λ ,

subject to 

λ R + λ M FM + λ B FB = Λ

σ LB ≤ λ B ≥ 0 σ LB ≥ 0 λ R

σ LM ≤ λ M ≥ 0 σ LM ≥ 0 λ B

σ HB ≤ λ B ≥ 0 σ HB ≥ 0 λ M

σ HM ≤ λ M ≥ 0 σ HM

 

 and complementary slackness conditions. 

It is possible to characterize a symmetric, pure-strategy equilibrium in which agents invest in 

both types of capacity and in the risk-free security ( > 0, > 0, > 0) λ R λ B λ M . In the symmetric 

equilibrium, N agents exclusively use baseload capacity to serve demand in the low-demand 

state, and they serve all incremental demand in the high-demand state with marginal capacity 

( ) σ HM = λ M . In both states, agents use all of their baseload capacity ( ) σ LB = σ HB = λ B , and they 

reserve marginal capacity in the low-demand state ( = 0) σ LM . In general, we cannot rule out 

asymmetric, pure-strategy equilibria. We can, however, impose enough structure so that the 

proposed symmetric equilibrium is unique among candidate symmetric equilibria. 

The equilibrium corresponds to the following graph of supply and demand: 
9

The following three lemmas yield the result. The first lemma indicates necessary supply-side 

conditions for the symmetric equilibrium. These supply-side conditions are not sufficient for 

yielding the proposed symmetric equilibrium and must be complemented with demand-side 

conditions. Even so, the supply-side conditions do restrict the best-replies that can obtain in any

pure-strategy equilibrium, symmetric or asymmetric. The second lemma indicates these 

restrictions. The third lemma indicates demand-side conditions in an environment featuring 

linear demands that rule out equilibria in which agents’ investments in baseload or marginal 

capacity are financially constrained. 

Lemma 1: A symmetric equilibrium in which each agent chooses ( ) λ R λ B λ M σ LB σ LM σ HB σ HM , , , , , ,

with > 0 λ R , > 0 λ B , > 0 λ M , σ LB = σ HB = λ B , = 0 σ LM and σ HM = λ M exists only if 

( )

( )( ) 1

2 1

1

≤ − +

− ≤

F F r

C C

B M

M B 

or, equivalently, 

( )( )( ) ( ) M B B M CM CB C − C ≤ F − F 1+ r ≤ 2 − . 

See Appendix 1. The result does not depend on the specific forms of the demand functions PL

and PH . 

Price 

Output 

Low Demand High Demand 

Supply 

PH

PL

CM

CB

ΣL = Nλ B ( ) Σ H = N λ B + λ M
10

Lemma 1 assures that all of the shadow prices in the proposed symmetric equilibrium are nonnegative. It indicates that neither marginal capacity nor baseload capacity can be too expensive 

relative to the other. Otherwise, agents might invest in only one type of capacity. 

More generally, one can show that a symmetric equilibrium in which agents only invest in 

marginal capacity exists only if ( )( ) ( ) B M CM CB F − F 1+ r ≥ 2 − , and a symmetric equilibrium in 

which agents only invest in baseload capacity exists only if (C C ) (F F )( )r M − B ≥ B − M 1+ . Now 

we state results that do not depend on either symmetry or strategic interdependence. 

Lemma 2: Given ( )( )( ) ( ) M B B M CM CB C − C < F − F 1+ r < 2 − , any one agent’s strategy 

( ) λ R λ B λ M σ LB σ LM σ HB σ HM , , , , , , in a pure-strategy equilibrium satisfies the following two 

conditions: 

(i) = = ≥ 0 λ B σ LB σ HB That is, given an agent invests in baseload capacity, the agent 

exhausts all of that capacity in both the low demand state and the high demand state. 

Agents do not let costly capacity go unused, and they use all of that capacity in both 

states. 

(ii) λ M = σ LM or λ M = σ HM . That is, given an agent invests in marginal capacity, that 

agent exhausts all of that capacity in at least one of the two demand states. Agents do 

not let costly (marginal) capacity go unused. 

See Appendix 2. Again, the result does not depend on the specific forms of the demand 

functions PL and PH . 

We now impose linear demand functions PL = α − βΣL and PH = + − βΣ H (α ε ) . The next 

lemma simply indicates that, given sufficiently large investment resources Λ, it makes sense for 

the agent to invest some resources in the risk-free asset. That is, it indicates a condition under 

which investments in baseload and marginal capacity are not financially constrained. 

Lemma 3: Given the premise of lemma 2, the system of linear demands, and 

M

M

B

B F C F C

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ −

+ ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ − Λ >

β

α

β

α

2 2 , then 

(i) > 0 λ R , 

(ii) = ( )( ) − − = 0 σ LMσ HM λ M σ LM λ M σ HM λ M . That is, σ LM and σ HM are not both greater 

than zero, but if one is positive, then the agent exhausts all of the marginal capacity in 

the corresponding state. 

Note that item (ii) in lemma 3 constitutes a refinement of item (ii) in lemma 2. See Appendix 3. 
11

We can now pose conditions under which the proposed symmetric equilibrium is the only 

symmetric equilibrium. Each agent’s maximand is weakly concave in that agent’s seven choice 

variables, and the constraints are convex (linear), so we can appeal to a concave programming 

theorem according to which the first-order conditions for a maximum characterize a global 

maximum. The various supply-side and demand-side conditions rule out other candidate 

symmetric equilibria as well as equilibria in which investments in production capacity are 

financially constrained. 

Proposition 1: Assume ( )

( )( ) 1

2 1

1

< − +

− <

F F r

C C

B M

M B , α > CM , F ( )r ε > B 1+ , and 

M

M

B

B F C F C

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ −

+ ⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ − Λ >

β

α

β

α

2 2 . For each agent let 

0 * σ LM = , 

( ){ } 0

( 1)

2( ) ( )(1 ) * * * >

+

− + − − − + = = = βα λ σ σ

N

C C C F F r M M B B M

B HB LB , 

( ) { } 0

( 1)

(1 ) 2 ( )(1 ) ( ) * * >

+

− + + − + − − = = βε λ σ

N

r FB FB FM r CM CB

M HM , and 

( ) { }( ) ( 1)

2( ) ( )

( 1)

(1 )

( 1)

( ) *

B M

M B B M

M

M

B

M

R F F

N

C C F F F

N

F r F

N

C − +− − − − +− + − +− = Λ − β βε

β

α λ . 

One can chooseΛ large enough so that 0 * λ R ≥ in which case the vector 

( ) * * * * * * * , , , , , , λ R λ B λ M σ LB σ LM σ HB σ HM corresponds to a global maximum of each agent’s program. 

Moreover, the profile of symmetric strategies constitutes the only symmetric equilibrium of 

the game. 

See Appendix 4. 

Remark 1: We determine the joint-profit maximizing (monopoly) benchmark by evaluating all 

of the results in Lemma 3 for N = 1. 

Remark 2: We determine the social optimum benchmark by substituting the maximand with 

() () ( ) ( ) L H B LB HB M LM HM R B FB M FM P d P d C C r

LB LM HB HM

σ σ σ σ σ σ σ σ λ λ λ

σ σ σ σ

+ − + − + + − − ∫ ∫

+ +

0 0
12

Most of the previous results obtain. (See Appendix 5.) Imposing the assumption of price-taking 

on the part of producers also yields the social optimum benchmark, although the prices that 

obtain in both the low demand state and the high demand state exceed the marginal costs of 

production. (See Figure 2.) 

4. Experimental Design5

The experiment features two treatments. In one treatment, subjects participated in a market 

featuring two production technologies and two demand conditions. The two technologies 

correspond to marginal and baseload capacity in the model, and the two demand conditions 

correspond to the two markets, L and H. The other treatment featured only one technology, but 

it also featured two demand conditions. 

The modeling presented here guided the design of the treatment with 2 technologies and 2 

markets (“Treatment 2”), and we crafted a similar model, not presented here, to guide the design 

of the treatment with 1 technology and 2 markets (“Treatment 1”). We used the supply-side and 

demand-side conditions indicated in lemmas 2 and 3 to choose parameters for Treatment 2, and 

we used the supply-side and demand-side conditions indicated by the other model to choose 

parameters for Treatment 1. 

The French experimental subjects were endowed with investment resources enumerated in an 

experimental currency called “Yen.” (Ironically enough, in the English-language version, 

subjects use an experimental currency called “Francs.”) Subjects could use their Yen to buy 

“tickets.” Subjects were informed that they could “redeem” any one ticket in one or both 

markets. Redeeming a ticket involved selling one unit of an experimental good called “units” in 

either or both of two markets. The markets were labeled “Market 1” and “Market 2.” Market 1 

corresponded to the low-demand state, and Market 2 corresponded to the high-demand state. 

Subjects could buy tickets from the experimenter at prices (“Purchase Prices”) the experimenter 

publicly posted. The experimenter also posted “Redemption Fees” – fees subjects would pay 

when and if subjects they chose to sell units. The Purchase Prices and Redemption Fees are 

analogous to fixed costs and marginal costs, respectively. 

The experiment did not feature active buyers on the demand side. Rather, the experimenter 

publicly posted a market demand schedule (“Redemption Schedule”) for each of the two 

markets. 

Treatment 2 featured two types of tickets labeled Blue Tickets (bleue) and White Tickets 

(blanche) The Purchase Prices and Redemption Fees were structured so that Blue Tickets were 

analogous to units of baseload capacity and that White Tickets were analogous to marginal 

capacity. Treatment 1 only featured one type of tickets, although these tickets also featured a 

Purchase Price and a Redemption Fee. 

 

5

 We include the experiment instructions in Appendix 6. We conducted the experimental sessions in Grenoble with 

a French translation of the instructions. 
13

The experimenter posted the purchase prices, redemption fees and redemption schedules before 

subjects made their investment and production decisions. Subjects then made investment 

decisions and production decisions for both markets simultaneously. Investment decisions 

involved decisions to buy tickets, and production decisions amounted to choosing how many 

units to sell in each market. The experimenter aggregated subjects’ choices and reported market 

outcomes. This sequence of posting prices, fees and schedules and aggregating decisions 

constituted a market period. We conducted several market periods in each of the six 

experimental sessions, and we did not vary the system parameters across sessions or across 

market periods. 

Figure 1 indicates the timing of decisions and payoffs in a market period, and Table 1 indicates 

the system parameters we used for the two treatments. Table 2 indicates investments, outputs 

and prices corresponding to three theoretical benchmarks. The benchmarks correspond to the 

equilibrium prediction (the “Cournot benchmark”), to the “Monopoly benchmark,” and to the 

“Social Optimum benchmark.” The entire demand system and the benchmarks for Treatment 2 

are graphically represented in Figure 2. The supply function indicates the supply that obtains 

under the Cournot benchmark. The demand system is comprised of two step-functions, one 

indicating low demand and the other indicating high demand. 

5. Results 

We conducted six experimental sessions between April 25 and October 3, 2005. Three sessions 

featured Treatment 1, and the other three sessions featured Treatment 2. All sessions featured 6 

subjects. All subjects participated in a number of practice market periods. The practice 

environment featured only one technology and one market. All subjects were engineering 

students at the Institut Nationale Polytechnique de Grenoble. Table 3 indicates the number of 

market periods we dedicated to Treatments 1 or 2 with each set of subjects. 

Most of the results we report here are static in that we ignore dynamic considerations such as 

“learning.” While it is reasonable to suggest that learning is an important phenomenon, it is 

interesting to suggest that treating realizations of output in each market period as independent 

events may nonetheless characterize the most important action. Indeed, the results are amenable 

to a simple, static narrative. Subjects behaviors exhibit much diversity, but, collectively, their 

behaviors are structured, stable, and susceptible to analysis with most orthodox and accessible 

game-theoretic concepts. Overall, they invest in capacity and dispatch capacity in ways 

consistent with the Nash-Cournot hypothesis. Even so, they do deviate from the Cournot 

benchmark in a systematic way. They tend to overinvest in marginal capacity and underinvest in 

baseload capacity. What this means is that consumers extract the Cournot-consistent surplus 

from the system but producers extract less than the Cournot-consistent surplus. They dissipate 

surplus by investing inefficiently; they could achieve Pareto improvements by investing less in 

marginal capacity and more in baseload capacity. 

The results indicate that the Cournot benchmark is a good indicator of outputs in both 

Treatments 1 and 2. This means that the Cournot benchmark also provides a good indicator of 

consumer surplus, since consumer surplus tracks output. Even so, this does not necessarily 
14

imply that the Cournot benchmark tracks producer surplus. Producer surplus also depends on the 

mix of subjects’ investments. It turns out that the Cournot benchmark is a good indicator of 

subjects’ investments in Treatment 1, but in Treatment 2 subjects consistently invest 

suboptimally, thus dissipating rents that they could otherwise have extracted for themselves. 

They consistently overinvest, relative to the Cournot benchmark, in marginal capacity and 

underinvest in baseload capacity. 

We should note that where the Cournot benchmark does perform well, behaviors do not 

converge on the Cournot benchmark but rather cycle around it. We will close this section by 

indicating a formative result about the manner in which behaviors cycle. The time series of 

producer surplus exhibit “sawtooth patterns,” patterns that are suggestive of Edgeworth Cycles 

(Maskin and Tirole 1988). Even though producer surplus cycles, total system efficiency is much 

more stable. Subjects consistently generate total surplus that falls just short of the total surplus 

consistent with the Cournot benchmark. 

We break down these conclusions into seven discrete results. 

Result 1: Outputs cycle around the Cournot benchmarks. 

Figure 3 displays the cycling of both outputs and prices in Session 4 around the Cournot 

benchmarks for both Markets L and H. Price is plotted against the axis on the left, and 

output is plotted against the axis to the right. A single, horizontal line indicates the 

Cournot benchmark of (price, output) = (200, 24) for Market H, and a single dashed line 

indicates the Cournot benchmark of (100, 18) for Market L. 

The time series of both prices and outputs do not “converge” to the benchmarks, although 

they do line up with the benchmarks at different times during the session. What is striking, 

however, are not the time series but the averages of these quantities, especially the averages 

of the outputs. 

In Table 4 we present tests of the differences between the benchmark outputs and averages 

of outputs in the six experimental sessions. The third and fourth columns indicate both the 

averages of and bootstrapped standard errors of the outputs from each of the six 

experimental sessions.6

 The other columns to the right indicate the theoretical benchmarks 

and t-statistics corresponding to tests of the differences between the benchmarks and 

averages of the outputs. 

Both the Monopoly and Optimum benchmark outputs are statistically distinguishable from 

the realized average outputs. The Cournot benchmarks are not statistically distinguishable 

in all 7 of 12 instances and outperform the Monopoly benchmark in all 12 instances. The 

average output lies closer to the Optimum than to the Cournot benchmark in only one 

instance (Session 2, Market L). In the other 11 instances, the Cournot benchmark performs 

better. 

 

6

 All standard errors and confidence intervals (not reported) we use in all of are exhibits were each generated from 

10,000 bootstrap samples. As few as 100 bootstrap samples would have done the trick. 
15

Result 2: Consumer surplus cycles around the Cournot benchmark. 

This result follows from the fact that output cycles around the Cournot benchmark. Even 

so, we present a complementary set of tests. Table 5 indicates t-tests for differences 

between average consumer surplus achieved in each session and the theoretical 

benchmarks. Again, we cannot statistically distinguish the Cournot benchmark from the 

actual surplus realized in each session. We can distinguish the Monopoly benchmark from 

the actual consumer surplus. The Social Optimum performs better than the Monopoly 

benchmark, but the Cournot benchmark dominates both benchmarks. 

Result 3: In Treatment 1, investment cycles around the Cournot benchmark. 

We present tests in Table 6. The investments in the first three sessions average about 24 

units – the number of units corresponding to the Cournot benchmark. In Sessions 1 and 2, 

these averages are not significantly different than the Cournot benchmark , but in session 3 

investment is significantly lower than the Cournot benchmark. Average capacity 

investment is sharply distinguishable from the Monopoly and Social Optimum benchmarks 

in all three sessions. 

Result 4: In Treatment 2, subjects overinvest in marginal capacity and underinvest in baseload 

capacity, but the Cournot benchmark provided some indication of the total number of units 

invested. 

Again, see Table 6. None of the benchmarks characterize investment separately in 

baseload capacity or marginal capacity in that subjects invest much more heavily in 

marginal capacity and much less heavily in baseload capacity than any one benchmark 

would predict, but the total investment is not distinguishable from the Cournot benchmark 

of 24 unites in session 4. In each of the three sessions 4, 5, and 6, total investment 

exceeded the Cournot benchmark. 

We speculate that subjects overinvested in marginal capacity, because some of them 

perceived fixed costs as marginal. Marginal capacity may have featured a fixed cost of 50 

francs, but the sum of the fixed and marginal costs was 110 francs. (See the system 

parameters in Table 1.) In contrast, baseload capacity may have featured a higher fixed 

cost of 100 francs, but the sum of the fixed and marginal costs was 120 francs. If one 

perceives the sum of fixed costs and marginal costs as marginal, then marginal capacity 

appears to dominate baseload capacity. 

Result 5: In Treatment 2, whether or not subjects have invested efficiently, they tend to dispatch 

capacity efficiently. 
16

Given subjects invest in some portfolio of baseload and marginal capacity, the prediction is 

that if they withhold any capacity, they withhold marginal capacity. Moreover, if they 

withhold capacity, they withhold capacity in the low demand state, Market L. 

Table 7 indicates the average number of units subjects withheld in both markets of each 

session. In the first three sessions, efficient dispatch entails withholding capacity in the 

Low Demand state and dispatching all capacity in the High Demand state. Sure enough, 

subjects withheld capacity in the low state and tended to dispatch all capacity in the high 

state. 

In Treatment 2 efficient investment entailed dispatching all baseload capacity in the law 

state and exhausting all baseload and marginal capacity in the high state. In Session 6 

subjects did not, on average, exhaust all baseload capacity in the low state or all marginal 

capacity in the high state, but in all other instances subjects tended to exhaust baseload and 

marginal capacity when they should, and they withheld marginal capacity in the low state. 

Result 6: Producer surplus cycles between the Cournot and Social Optimum benchmarks, and it 

cycles in a manner suggestive of Edgeworth Cycles. 

In Figure 4 we represent the time series of producer surplus from Session 4. Producer 

surplus tends to decline to the Social Optimum benchmark over a number of period before 

rebounding in a single period to the Cournot benchmark. 

The pattern is suggestive of “sawtooth patterns” or Edgeworth Cycles others have observed 

in markets such as those for retail gasoline. (See, for example, Noel 2004.) In retail 

gasoline markets, prices tend to fall slowly before rebounding to a higher level and slowly 

falling again. 

It would be hard to suggest that the price data generated in this experiment exhibit 

Edgeworth Cycles, but producer surplus provides a way of aggregating the performance of 

more than one market in a single metric. It is this single metric, producer surplus, that 

exhibits sawtooth patterns. 

The time series of producer surplus exhibited in the other 5 sessions do not necessarily 

exhibit such a clear pattern, although those data are also suggestive. The results, suggest 

that cycling patterns may yet be amenable to further experimental analysis. 

Result 7: Total system stability and efficiency. 

The time series of total system efficiency is more stable than the series or either producer 

surplus or consumer surplus – a reflection of the fact that consumer surplus and producer 

surplus are negatively correlated. (See Figure 5.) The standard errors of total surplus are 

much smaller than the standard errors that correspond to consumer surplus. Compare, for 

example, the standard errors reported in Table 8 to those reported in Table 5. 
17

In both treatments, system efficiency falls short of the Cournot benchmark and is sharply 

distinguishable from the Monopoly benchmark. The Social Optimum benchmark indicates 

the surplus available in the system. In each of the six sessions, system efficiency exceeds 

94%. In Table 8 we indicate the total surplus extracted, on average, in each session. 

System efficiency falls short of the Cournot benchmark and is statistically distinguishable 

from all three benchmarks. 

6. Conclusion 

The experimental framework constitutes a first step in crafting a richer platform for exploring 

how investment interacts with market power, market structure, and market institutions. The 

existing framework features the simplest environment we could construct for exploring an 

investment problem that involves more than one production technology. We used it to generate 

accessible, discrete results. The results are interesting in themselves, but, more importantly, they 

provide a baseline against which to compare results in richer environments. 

The framework can be adapted for examining investment behaviors in richer environments. We 

can, for example, adapt the framework to explore how investment varies across different market 

structures and different institutional landscapes. Specifically, we can add more and differentlyendowed players, and we can use different market institutions. The framework can also be 

adapted to include investment in innovation. Parties might, for example, not merely invest in 

production capacities but may devote some resources to developing and commercializing new 

production technologies. 

Making the environment richer leads to questions about how far baseline results scale up. As 

John Ledyard (1993) suggests by metaphor, results achieved with scaled-down models in the 

engineer’s wind tunnel or towing tank may not correspond to those achieved with full-size 

prototypes in the open skies or open waters. Experimental planes crash and vessels capsize. But 

there are two motivations for starting small and simple. First, more ambitious experiments and 

research might frustrate efforts to identify and isolate baseline effects that do scale up. 

Researchers might become distracted by noise produced in richer environments and miss the 

fundamental action. Second, baseline results can inform analysis of results achieved in richer 

environments. With baseline results in hand, one can always pose the hypothesis that baseline 

results have scaled up and can work out implications from there. The hypothesis is interesting if 

it helps to reveal new results. It is also interesting if posing it leads to implausible conclusions, 

in which case one might accept the alternative hypothesis that results do not scale up. 

The existing framework has allowed us to yield a number of simple, baseline results. Parties 

dispatch capacity efficiently, but they do not invest efficiently. They overinvest in “marginal” 

capacity and underinvest in “baseload” capacity. Inefficient investment depresses total system 

efficiency. Even so, consumers extract the surplus that is available to them in the game-theoretic 

benchmark. The game-theoretic benchmark itself is interesting, because it helps distinguish 

outcomes from alternative benchmarks. These benchmarks include a monopoly benchmark and 

a socially optimal benchmark. 
18

The results suggest some immediate follow-on research. First, sequencing output decisions in 

the two markets might allow subjects to figure out how to invest more efficiently. Specifically, 

we would allow them to experience outcomes in one of the two markets before making output 

decisions for the second market. Would they be better able to internalize the nature of fixed and 

marginal costs in the system? Second, the existing framework is amenable to exploring dynamic 

oligopoly. Sawtooth patterns manifest themselves in certain measures of system performance. 

Generating longer times series can allow us to investigate the robustness of these patterns and to 

suggest explanations for them that might inform theories of oligopolistic competition. 
19

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21

Figure 1 

Timing of Subjects’ Decisions in One Market Period

Figure 2 

Treatment 2: The Experimental System 

and Theoretical Benchmarks

Cournot Low

Cournot High

Optimum Low

Optimum High

Monopoly Low

Monopoly High

0

100

200

300

400

500

600

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54

Volume

Price (Francs) . Subjects make investment 

and production choices 

Subjects observe prices Subjects calculate 

period’s payoff 
22

Figure 3 

Session 4

Treatment 2

0

100200300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

Market Period

Price (Francs) .

Cournot High Cournot Low Price High Price Low Output High Ou
23

Figure 4 

Producer Surplus

Session 4

0

1,0002,0003,0004,0005,0006,0007,0008,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

Market Period

Price (Francs) .

Monopoly Cournot Social Optimum Producer Surplus
24

Figure 5 

Total Surplus and Consumer Surplus

0

2,0004,0006,0008,000

10,00012,00014,000

Session

1

Session

2

Session

3

Session

4

Session

5

Experimental Session

Price (Francs) 

Total Surplus Cournot T.S. Consumer Surplus Cournot C.S.
25

Table 1 

System Parameters*

 Treatment 1 Treatment 2

 

 Demand-side Parameters Intercept L α 340 460

Intercept H α + ε 680 680

 Slope β 20 20

 

 Supply-side Parameters 

 Baseload Capacity Purchase Price FB 100

 Redemption Fee CB 20

 

 Marginal Capacity Purchase Price FM 50 50

 Redemption Fee CM 60 60

 

 Endowment Λ 1000 1000

 

Risk-free rate of 

return r 20% 20%

 

* The quantities are denominated in the experimental currency.
26

Table 2 

Theoretical Benchmarks* 

Investments Output 

 Low Demand State High Demand S Total  Baseload Marginal Baseload Marginal Output Price Baseload Marginal OTreatment 1  Cournot 24 12 12 100 24  Monopoly 14 7 7 200 14  Social Optimum 28 14 14 60 28 Treatment 2  Cournot 18 6 18 18 100 18 6  Monopoly 10 4 10 10 260 10 4  Social Optimum 21 7 21 21 40 21 7 

*

Prices are denominated in the experimental currency, Output is denominated in units of output, 

and Investments are denominated in units of production capacity. Each unit of production capacity 

can produce as many as two units, one in the low-demand state, and one in the high-demand state.
27

Table 3 

Number of Market Periods in each Experimental Session 

 

Experimental 

Session Treatment Market Periods 

 

1 1 21 

2 1 25 

3 1 22 

4 2 20 

5 2 13 

6 2 17 

 
28

Table 4 

Average Outputs 

Average Outputs Benchmarks & t-statistics 

& Standard Errors Cournot Monopoly Op

Session Obs Mkt L Mkt H Mkt L Mkt H Mkt L Mkt H Mkt L 

1 20 12.15 24.15 12.00 24.00 7.00*** 14.00*** 14.00**

 0.60 0.82 0.25 0.18 8.65 12.36 -3.11 

2 25 13.56 23.68 12.00*** 24.00 7.00*** 14.00*** 14.00 

 0.45 0.53 3.43 -0.61 14.42 18.38 -0.97 

3 22 12.36 22.59 12.00 24.00*** 7.00*** 14.00*** 14.00**

 0.33 0.40 1.10 -3.53 16.24 21.51 -4.95 

Pooled 67 12.75 23.46 12.00*** 24.00 7.00*** 14.00*** 14.00**

1-3 0.28 0.35 2.66 -1.54 20.46 27.16 -4.46 

4 20 18.15 24.05 18.00 24.00 10.00*** 14.00*** 21.00**

 0.51 0.72 0.30 0.07 16.07 13.87 -5.62 

5 13 16.77 24.54 18.00* 24.00** 10.00*** 14.00*** 21.00**

 0.72 0.26 -1.72 2.09 9.47 40.90 -5.92 

6 17 16.12 24.47 18.00*** 24.00 10.00*** 14.00*** 21.00**

 0.60 0.58 -3.15 0.82 10.25 18.17 -8.18 

Pooled 50 17.10 24.32 18.00** 24.00 10.00*** 14.00*** 21.00**

4-6 0.37 0.35 -2.46 0.90 19.42 29.17 -10.66 

The notations ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 

Tests based on bootstrapped confidence intervals or bootstrapped standard errors yield the same results.
29

Table 5 

Consumer Surplus 

 

 Averages Consumer Surplus Benchmarks & t-statistics 

 & Standard Errors 

Session Obs Cournot Monopoly Optimum 

 

1 20 7,133 6,840 2,240*** 9,380*** 

 498 0.59 9.82 -4.51 

 

2 25 7,181 6,840 2,240*** 9,380*** 

 229 1.49 21.57 -9.60 

 

3 22 6,341 6,840** 2,240*** 9,380*** 

 196 -2.54 20.91 -15.49 

 

Pooled 67 6,891 6,840 2,240*** 9,380*** 

1-3 190 0.27 24.45 -13.09 

 

4 20 8,810 8,580 2,720*** 11,760***

 398 1.33 15.29 -7.41 

 

5 13 8,495 8,580 2,720*** 11,760***

 280 -0.30 20.60 -11.64 

 

6 17 8,293 8,580 2,720*** 11,760***

 428 -0.67 13.03 -8.11 

 

Pooled 50 8,552 8,580 2,720*** 11,760***

4-6 229 -0.12 25.43 -13.99 

 

The notations ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 

Tests based on bootstrapped confidence intervals or bootstrapped standard errors yield the same results.
30

Table 6 

Capacity Investments 

Average Investments Benchmarks & t-statistics 

& Standard Errors Cournot Monopoly 

Session Obs Baseload Marginal Total Baseload Marginal Total Baseload Marginal Total Baseload 

1 20 24.35 24.35 24.00 24.00 14.00*** 14.00*** 

 0.78 0.78 0.45 0.45 13.34 13.34 

2 25 23.92 23.92 24.00 24.00 14.00*** 14.00*** 

 0.54 0.54 -0.15 -0.15 18.32 18.32 

3 22 22.59 22.59 24.00*** 24.00*** 14.00*** 14.00*** 

 0.40 0.40 -3.55 -3.55 21.64 21.64 

Pooled 67 23.61 23.61 24.00 24.00 14.00*** 14.00*** 

1-3 0.35 0.35 -1.11 -1.11 27.56 27.56 

4 20 4.30 20.20 24.50 18.00*** 6.00*** 24.00 10.00*** 4.00*** 14.00*** 21.00*** 

 0.70 1.09 0.64 -19.62 13.00 0.78 -5.70 14.83 16.38 -23.91 

5 13 6.69 18.08 24.77 18.00*** 6.00*** 24.00*** 10.00*** 4.00*** 14.00*** 21.00*** 

 0.51 0.61 0.31 -22.02 19.69 2.45 -3.31 22.95 34.29 -27.86 

6 17 10.59 14.65 25.24 18.00*** 6.00*** 24.00** 10.00 4.00*** 14.00*** 21.00*** 

 0.61 0.92 0.60 -12.06 9.36 2.07 0.59 11.52 18.80 -16.95 

Pooled 50 7.06 17.76 24.82 18.00*** 6.00*** 24.00** 10.00*** 4.00*** 14.00*** 21.00*** 

4-6 0.54 0.66 0.34 -20.31 17.93 2.45 -2.94 20.98 32.26 -25.88 

The notations ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 

Tests based on bootstrapped confidence intervals or bootstrapped standard errors yield the same results.
31

Table 7 

Proportions of Capacity Withheld 

 

 Average Withholding & 

 Standard Errors 

 

 Market L Market H 

Session Obs Baseload Marginal Baseload Marginal 

 

1 20 12.20*** 0.20** 

 0.54 0.09 

 

2 25 10.36*** 0.24 

 0.83 0.14 

 

3 22 10.23*** 0.00 

 0.50 0.00 

 

Pooled 67 10.87*** 0.15** 

1-3 0.41 0.06 

 

4 20 0.00 6.35*** 0.00 0.45 

 0.00 0.61 0.00 0.27 

 

5 13 0.15 7.85*** 0.15 0.08 

 0.15 0.71 0.10 0.07 

 

6 17 1.47*** 7.65*** 0.06 0.71*** 

 0.25 0.56 0.06 0.16 

 

Pooled 50 0.54*** 7.18*** 0.06 0.44*** 

4-6 0.13 0.37 0.03 0.13 

 

The notations ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 

The significance tests depend on bootstrapped confidence intervals, not on the reported standard errors.
32

Table 8 

Total System Efficiency 

(Total Surplus) 

 

 Average Total Surplus Benchmarks & t-statistics 

 & Standard Errors 

Session Obs Cournot Monopoly Optimum 

 

1 20 10,237 10,440** 8,340*** 10,580***

 91 -2.23 20.86 -3.77 

 

2 25 10,303 10,440*** 8,340*** 10,580***

 36 -3.83 54.90 -7.74 

 

3 22 10,273 10,440*** 8,340*** 10,580***

 40 -4.19 48.36 -7.69 

 

Pooled 67 10,273 10,440*** 8,340*** 10,580***

1-3 33 -5.03 58.43 -9.26 

 

4 20 12,333 12780,*** 10,040*** 12,960***

 93 -4.80 24.64 -6.74 

 

5 13 12,435 12780,*** 10,040*** 12,960***

 43 -8.04 55.91 -12.25 

 

6 17 12,351 12780,*** 10,040*** 12,960***

 63 -6.80 36.58 -9.65 

Pooled 50 12,363 12780,*** 10,040*** 12,960***

4-6 45 -9.20 51.18 -13.16 

 

The notations ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. 

Tests based on bootstrapped confidence intervals or bootstrapped standard errors yield the same results.
33

Appendix 1 

The corresponding Lagrangian is 

( )

( )

LB LB LM LM HB HB HM HM

LB B LB LM M LM HB B HB HM M HM

R R B B M M

R B B M M

R B B M M

H HB HM H HB HM B HB M HM

L LB LM L LB LM B LB M LM

F F

r F F

P C C

L P C C

γ σ γ σ γ σ γ σ

μ λ σ μ λ σ μ λ σ μ λ σ

μ λ μ λ μ λ

μ λ λ λ

λ λ λ

σ σ σ σ σ σ

σ σ σ σ σ σ

+ + + +

+ − + − + − + −

+ + +

+ Λ − − −

+ − −

+ + + Σ + − −

= + + Σ + − −

−

−

( ) ( ) ( ) ( )

( )

( )

( )

The Kuhn-Tucker conditions are: 

(7) ( ) 0

(6) ( ) 0

(5) ( ) 0

(4) ( ) 0

(3) (1 ) 0

(2) (1 ) 0

(1) 0

= ′ ⋅ + + − − + = ∂

∂

= ′ ⋅ + + − − + = ∂

∂

= ′ ⋅ + + − − + = ∂

∂

= ′ ⋅ + + − − + = ∂

∂

= − + + + + = ∂

∂

= − + + + + = ∂

∂

= − + = ∂

∂

H HB HM H M HM HM

HM

H HB HM H B HB HB

HB

L LB LM L M LM LM

LM

L LB LM L B LB LB

LB

M LM HM M

M

B LB HB B

B

R

R

P P C L

P P C L

P P C L

P P C L

F L

F L

r

L

σ σ μ γ

σ

σ σ μ γ

σ

σ σ μ γ

σ

σ σ μ γ

σ

μ μ μ μ λ

μ μ μ μ λ

μ μ λ

and λ R + λ M FM + λ B FB = Λ

σ LB ≤ λ B ≥ 0 σ LB ≥ 0 λ R

σ LM ≤ λ M ≥ 0 σ LM ≥ 0 λ B

σ HB ≤ λ B ≥ 0 σ HB ≥ 0 λ M

σ HM ≤ λ M ≥ 0 σ HM

 

and complementary slackness conditions. 
34

The equilibrium conditions and the complementary slackness conditions indicate conditions on 

the multipliers: 

= 0 σ LM implies ≥ 0 LM γ and = 0 μ LM

σ LB = λ B implies ≥ 0 μ LB and = 0 LB γ

σ HB = λ B implies ≥ 0 μ HB and = 0 HB γ

σ HM = λ M implies ≥ 0 μ HM and = 0 HM γ

> 0, > 0, > 0 λ R λ B λ M imply = = = 0 μ R μ B μ M

These conditions and (1) imply μ = r. Subtracting (2) from (3) and rearranging yields 

( )( ) − 1+ = + − > 0 B M LB HB HM F F r μ μ μ (8) 

Subtracting (5) from (4) and rearranging yields ( − ) = + > 0 CM CB LB LM μ γ (9) 

Subtracting (6) from (7) and rearranging yields ( − ) = − > 0 CM CB μ HB μ HM (10) 

Adding (9) and (10) yields ( ) ( ) CM CB LB HB HM LM 2 − = μ + μ − μ +γ (11) 

Substituting (8) into (11) and rearranging yields 

2( ) ( )( ) − − − 1+ = ≥ 0 M B B M LM C C F F r γ (12) 

or ( )

( )( ) F F r

C C

B M

M B

− +

− ≤

2 1

1 (13) 

Similarly, substituting (8) into (10) yields 

( ) ( )( ) − − − 1+ = − ≤ 0 M B B M LB C C F F r μ (14) 

or ( )

( )( ) 1

1

≤ − +

−

F F r

C C

B M

M B (15) 

Inequalities (13) and (15) achieve the result. 

Remark: All of the other multipliers are non-negative. We only need to solve for μ HB and μ HM . 

Equations (2) and (12) yield = (1+ ) + ( − ) > 0 HB M CM CB μ F r . Also, equation (3) yields 

= F ( ) 1+ r > 0 μ HM M
35

Appendix 2 

Under the system of linear demands, the first seven of the agent’s first-order conditions become: 

(7) ( ) 2 ( ) 0

(6) ( ) 2 ( ) 0

(5) 2 ( ) 0

(4) 2 ( ) 0

(3) (1 ) 0

(2) (1 ) 0

(1) 0

= + − + − Σ − − + = ∂

∂

= + − + − Σ − − + = ∂

∂

= − + − Σ − − + = ∂

∂

= − + − Σ + − − + = ∂

∂

= − + + + + = ∂

∂

= − + + + + = ∂

∂

= − + = ∂

∂

−

−

−

−

HB HM H M HM HM

HM

HB HM H B HB HB

HB

LB LM L M LM LM

LM

LB LM L B LB LB

LB

M LM HM M

M

B LB HB B

B

R

R

C L

C L

C L

C L

F L

F L

r

L

α ε β σ σ β μ γ

σ

α ε β σ σ β μ γ

σ

α β σ σ β μ γ

σ

α β σ σ β μ γ

σ

μ μ μ μ λ

μ μ μ μ λ

μ μ λ

Equation (1) implies R μ = r + μ . Now, subtracting equation (3) from (2) and substituting 

R μ = r + μ yields 

( )( ) ( ) − 1+ + = + + − ( + + ) > 0 B M R LB HB B LM HM M F F r μ μ μ μ μ μ μ

Subtracting (4) from (5) yields 

( ) − = − − + > 0 CM CB LB LM LB LM μ μ γ γ

Taking the difference of these last two expressions and rearranging yields 

( )( ) − 1+ − ( − )( ) = + + − ( + + )− ( − ) > 0 B M CM CB HB LB B HM LM M FB FM R F F r μ γ μ μ γ μ μ

which implies ( ) + + > 0 HB LB μ B μ γ . This last expression and complementary slackness imply 

( ) − = 0 λ B σ HB σ LB λ B . (A) 

This last expression (A) implies that given an agent dispatches baseload capacity in the low state, 

the agent also exhausts all of that baseload capacity in the high state. 
36

Now, by similar reasoning, subtracting the difference between (6) from (7) from the difference 

between (3) and (2) yields 

( )( ) − 1+ − ( − )( ) = + + − ( + + )− ( − ) > 0 B M CM CB LB HB B LM HM M FB FM R F F r μ γ μ μ γ μ μ

which implies ( ) + + > 0 HB LB μ B μ γ . This last expression and complementary slackness imply 

( ) − = 0 λ B σ LB σ HBλ B . (B) 

This last expression implies that given an agent dispatches baseload capacity in the high state, 

the agent also exhausts all of that baseload capacity in the low state. Thus, feasibility constraints 

and equations (A) and (B) together imply result (i): = = ≥ 0 λ B σ LB σ HB . 

To establish result (ii), we simply examine equation (3). Equation (3) indicates that 

μ + μ + μ = (1+ μ) > 0 LM HM M FM

This last inequality and complementary slackness conditions imply 

( )( ) − − = 0 λ M σ LM λ M σ HM λ M . (C) 

Thus, either λ M = σ LM or λ M = σ HM . 

 
37

Appendix 3 

We again take the difference between equations (4) and (5) and add to it the difference between 

(6) and (7). We then subtract from this sum the difference between (2) and (3) to yield 

2( ) ( )( ) − − − 1+ = ( + + ) − ( + + )( ) + − > 0 M B B M LM HM M LB HB B FB FM R C C F F r γ γ μ γ γ μ μ

This last expression implies ( ) + + + ( − ) > 0 LM HM μ M FB FM μ R γ γ or 

( ) = 0 σ LMσ HM λ M λ R (D) 

Equation (D) indicates that given agent invests some resources in the risk-free asset (in which 

case > 0 λ R ) and given the agent invests in some marginal capacity, the agent does not dispatch 

that capacity in at least one of the two demand states. 

We now state without proof that one can choose Λ large enough so that the agent invests some 

resources in the risk-free asset, in which case > 0 λ R , = 0 μ R and the last inequality becomes 

( ) + + > 0 LM HM μ M γ γ . Appealing to complementary slackness implies 

= 0 σ LMσ HM λ M or, simply, = 0 σ LMσ HM . 

This last expression and equation (C) achieve the result. 
38

Appendix 4 

By lemmas 2 and 3, there are only 6 candidate symmetric equilibria to consider. Each agent’s 

strategies in the six candidate symmetric equilibria correspond to 

 (i) = = = 0 λ B σ LB σ HB (iv) = = > 0 λ B σ LB σ HB

 = = = 0 λ M σ LM σ HM = = = 0 λ M σ LM σ HM

 (ii) = = = 0 λ B σ LB σ HB (v) = = > 0 λ B σ LB σ HB

 = > 0, = 0 σ LM λ M σ HM = > 0, = 0 σ LM λ M σ HM

 (iii) = = = 0 λ B σ LB σ HB (vi) = = > 0 λ B σ LB σ HB

 = 0, = > 0 σ LM σ HM λ M = 0, = > 0 σ LM σ HM λ M

Case (vi) corresponds to the proposed equilibrium, which we substantiate now. By lemma 3, we 

assume that Λ is large enough so that = 0 μ R . 

We also impose = = = = = = = 0 B M LM LM LB HB HM μ μ μ σ γ γ γ and must solve for 

, , , , , , , , , , LB HB HM LB HB HM LM R B μ μ μ μ σ σ σ γ λ λ and . λ M We also impose σ LB = σ HB = λ B and 

σ HM = λ M . 

The first seven first-order conditions become 

(7) ( ) 2 ( ) 0

(6) ( ) 2 ( ) 0

(5) 2 0

(4) 2 0

(3) (1 ) 0

(2) (1 ) 0

(1) 0

= + − + − Σ − − = ∂

∂

= + − + − Σ − − = ∂

∂

= − − Σ − + = ∂

∂

= − − Σ + − − = ∂

∂

= − + + = ∂

∂

= − + + + = ∂

∂

= − = ∂

∂

−

−

−

−

HB HM H M HM

HM

HB HM H B HB

HB

LB L M LM

LM

LB L B LB

LB

M HM

M

B LB HB

B

R

C L

C L

C L

C L

F L

F L

r

L

α ε β σ σ β μ

σ

α ε β σ σ β μ

σ

α βσ β γ

σ

α βσ β μ

σ

μ μ λ

μ μ μ λ

μ λ
39

Equation (1) implies μ = r . (6) and (7) imply ( ) ( ) CM − CB = μ HB − μ HM , and (3) implies 

F (1 r) μ HM = M + . These last two expressions yield (1 ) ( ) HB M CM CB μ = F + r + − . 

Equation (2) yields LB B HB μ = F (1+ r) − μ . Substitution into the previous expression yields 

( )(1 ) ( ) LB B M CM CB μ = F − F + r − − . 

Equations (4) and (5) yield LM CM CB μ LB γ = ( − ) − . Again, substitution into the previous 

expression yields 2(C C ) (F F )(1 r) γ LM = M − B − B − M + . 

Both μ LB and LM γ are strictly positive so long as ( )

( )( ) 1

2 1

1

< − +

− <

F F r

C C

B M

M B . 

Equation (4) alone yields − + − ( + Σ ) − − ( − )(1+ ) + ( − ) = 0 LB LB −L B B M CM CB βσ α β σ C F F r . 

Rearranging yields one of an agent’s reaction functions: 

( ) β

α β

σ

2

( )(1 ) ( ) L B M M B B

LB L

− Σ − F − F + r + C − C − C Σ = −

− . 

Also () () () λ B Σ−L = σ HB Σ−L = σ LB Σ−L . 

In the symmetric equilibrium, ( ) L N σ LB Σ = −1 − , ( )( ) Σ−H = N − σ HB +σ HM 1 , and the equilibrium 

level of baseload capacity dispatched by each agent in the low state is 

( )

0

( 1)

2( ) ( )(1 )

( 1)

2( ) ( )(1 ) * * *

>

+

− − − +

>

+

− + − − − + = = =

β

β

α λ σ σ

N

C C F F r

N

C C C F F r

M B B M

M M B B M

B HB LB

. 

We achieve the last two inequalities by imposing α > CM . 

Equation (6) implies − 2 ( + ) + ( + ) − Σ − − = 0 HB HM β −H CB μ HB β σ σ α ε . Substituting μ HB and 

rearranging yields the reaction function 

( ) β

ε β

σ

2

( ) 2( ) ( )(1 ) (2 ) , L H M B B M M

HM L H

+ Σ − Σ − C − C + F − F + r − + r F Σ Σ = − −

− − . 

Also ( )( ) M Σ−L Σ−H = HM Σ−L Σ−H λ , σ , . So, in equilibrium 
40

( ) { }

{ } 0

( 1)

2 ( )(1 ) ( )

( 1)

(1 ) 2 ( )(1 ) ( ) * *

>

+

− + − − >

+

− + + − + − − = =

β

β

ε λ σ

N

F F r C C

N

F r F F r C C

B M M B

B B M M B

M HM

We achieve the last two inequalities by imposing F ( r) ε > B 1+ . 

Finally, 

( ) { }( ) ( 1)

2( ) ( )

( 1)

(1 )

( 1)

( )

* * *

B M

M B B M

M

M

B

M

R B B M M

F F

N

C C F F F

N

F r F

N

C

F F

− +

− − − − +− + − +− = Λ −

= Λ − −

β β

ε

β

α

λ λ λ

The term * λ R is a function of α and ε but is increasing in Λ, so one can choose a value of Λ so 

that * λ R is non-negative. 

The maximand is (weakly) concave in the choice variables, and the constraints are linear in the 

choice variables. We conclude that the first-order conditions not only satisfy second-order 

conditions for a local maximum but also characterize a global maximum. 

We now outline how to rule out the five other candidate symmetric equilibria. One can break 

candidate equilibria (i) and (ii) by indicating that a single agent can profitably (and optimally) 

deviate by setting ( ) [ ( )]

β

α ε λ σ

2

C F 1 r M M

M HM

− + − + = = . 

In candidate equilibrium (iii), one can show that ( − ) = − ≤ 0 CM HM α γ , which contradicts the 

premise that α > CM . Solving for λ B in candidate equilibrium (iv) yields < 0 λ B , another 

contradiction. Finally, solving for λ M in candidate equilibrium (v) yields < 0 λ M . 
41

Appendix 5

The first-order conditions are almost identical. The difference is that we substitute equations (4) 

to (7) with simpler expressions: 

(7) 0

(6) 0

(5) 0

(4) 0

= − − + = ∂

∂

= − − + = ∂

∂

= − − + = ∂

∂

= − − + = ∂

∂

H M HM HM

HM

H B HB HB

HB

L M LM LM

LM

L B LB LB

LB

P C L

P C L

P C L

P C L

μ γ

σ

μ γ

σ

μ γ

σ

μ γ

σ

As before, we get the inequalities ( ) 1

2 (1 )

1

< − +

− <

F F r

C C

B M

M B . 

This means that a Central Planner also invests in both baseload capacity and marginal capacity 

so long as the inequalities hold. All the other results on multipliers obtain. 

We also yield 

[ ]

β

α λ σ σ

C (C C ) (F F )(1 r) B M B B M

B LB HB

− + − − − + = = =

[ ]

β

ε λ σ2(C C ) (F F )(1 r) F (1 r) M B B M M

M HM

− − − − + − + = =
42

Appendix 6 

Instructions 

This is an experiment in the economics of market decision-making. Various research 

foundations have provided funds for this research. The instructions are simple, and, if you 

follow them carefully and make good decisions, you might earn a considerable amount of money 

which will be paid to you in cash. 

In this experiment we are going to conduct a market for fictional items we will call “units.” You 

will participate in markets for units in a sequence of Market Periods. 

In each Market Period you may sell units. The number of units you may sell in each Market 

Period will depend on actions you take before each Market Period opens. 

Before each Market Period opens, the experimenter will grant you 1,000 units of a currency 

called francs. You will use francs to buy from the experimenter certificates to sell units in 

markets. In any one Market Period, your purchases of certificates may not exceed 1,000 francs. 

Each certificate you buy will enable you to sell units in markets. As many as two markets may 

be open in any one Market Period. Each certificate will enable you to sell one unit in each of the 

markets open in any one Market Period. So, for example, if two markets are open in a Market 

Period, any one certificate you buy will enable you to sell as many as two units, one in each of 

the two markets. 

For every 10 francs you do not use to buy certificates, the experimenter will pay you 2 francs. 

You will also keep the francs you do not use. If, for example, you choose not to buy any 

certificates in a given Market Period, you will keep the 1,000 francs the experimenter will have 

given you, and you will earn another 200 francs. Similarly, if you use 400 francs to buy 

certificates, 600 francs would remain unused. Your earnings would include whatever you gain 

from participating in the market plus 120 francs for the 600 francs you did not use to buy 

certificates. You would also keep the 600 francs. 

Selling a unit will involve three actions, the first two of which you control. First you must buy

certificates. Second, you can redeem certificates by using them to sell units in markets. Third, 

the experimenter will determine the value at which units offered for sale are redeemed. 

Certificates you do not use will be redeemed at a value of zero. 

You will buy and redeem certificates by filling out a simple form. On the form you will indicate 

the number of certificates you would like to buy and the number of certificates you would like to 

redeem in the markets. 

To inform your decisions to buy and redeem certificates, the experimenter will publicly post 

three sets of information: a schedule of redemption values at which certificates can be redeemed 

in the markets, a schedule of purchase prices at which certificates can be purchased, and a 

schedule of redemption fees you must pay for each certificate you choose to redeem. Your 
43

payoff from selling units will equal redemption values minus purchase prices minus redemption 

fees. 

The experimenter may indicate as many as two types of certificates that you may buy. The 

experimenter may sell certificates of one type for a price that is different than the price of 

certificates of the other type. Also, participants may redeem one type of certificate for a fee that 

is different than the fee applied to the other type of certificate. 

The experimenter will post a table indicating the prices at which certificates can be purchased 

and the fees at which they can be redeemed. In the example indicated below, the experimenter 

indicates two types of certificates labeled “Left-hand Certificates” and “Right-hand Certificates.” 

Left-hand Certificates each sell for a price of 30 francs, and Right-hand Certificates each sell for 

a price of 70 francs. Each Left-hand Certificates can be redeemed in a market for a fee of 50 

francs, and each Right-hand Certificate can be redeemed for a fee of 10 francs. 

The experimenter will post a Schedule of Redemption Values that indicates the prices at which 

certificates can be redeemed in the markets. The values may depend on the number of 

certificates offered for sale by all participants. All certificates redeemed by all participants in a 

particular market will be redeemed at the same value. So, for example, if participants collectively 

redeem 10 certificates in a market, then the schedule will indicate a uniform redemption value 

for each of those 10 certificates. If participants collectively redeem 20 certificates, then the 

schedule will indicate another uniform redemption value. The value at which each of 20 

certificates may be redeemed may be different than the value at which 10 certificates may be 

redeemed. 

In the example indicated below, the Schedule of Redemption Values indicates that a single 

certificate would be redeemed at a value of 500 francs in the event only one participant offers to 

redeem only one certificate. If, altogether, participants redeem two certificates, then each of the 

two certificates will be redeemed at a value of 480 francs. If, altogether, participants redeem 

three certificates, then each of the three certificates will be redeemed at a value of 460 francs. 

And so on. 

Left-hand 

Certificates

Right-hand 

Certificates

Purchase Price 30 70

Redemption Fee 50 10
44

The Schedule of Redemption Values may or may not change from one Market Period to the next. 

In some Market Periods the experimenter may conduct two markets labeled Market 1 and Market 

2. Both Market 1 and Market 2 will feature a Schedule of Redemption Values, and the 

redemption values in these two markets may be different. 

The experimenter will publicly post both Schedules of Redemption Values. One schedule will 

be labeled “Market 1.” (as above), and the other will be labeled “Market 2.” Participants will 

decide how many certificates of each type to buy, and then they will decide how many 

certificates of each type to redeem in each of the two markets. The experimenter will then 

aggregate all participants’ decisions for the two markets and will determine the redemption 

values that prevail in each of the two markets according to the Schedules of Redemption Values. 

Participants will indicate their decisions on a Certificate Redemption Form. On this form you 

will first indicate the number of certificates you will buy. In some markets, you will only have 

the option of buying Left-hand Certificates. In others you will have the option of buying both 

Left-hand and Right-hand Certificates. You will also indicate on this form the number of Lefthand Certificates and (when available) the number of Right-hand Certificates you would like to 

redeem in Market 1. Some market periods will include a second market, Market 2. In these 

instances, you will also indicate the number of Left-hand and Right-hand Certificates you would 

like to redeem in Market 2. 

Number of 

Certificates Redeemed

Redemption

Value

1 500

2 480

3 460

4 440

5 420

6 400

7 380

8 360

9 340

10 320

. .

. .

Schedule of Redemption Values

Market 1
45

You will indicate how many certificates you would like to buy and to redeem by circling your 

choices on the Certificate Redemption Form. On the Certificate Redemption Form there are as 

many as six choices to make: (1) the number of Left-hand Certificates you would like to buy, (2) 

the number of right-hand Certificates you would like to buy, (3) the number of Left-hand 

Certificates you would like to redeem in Market 1, (4) the number of Right-hand Certificates you 

would like to redeem in Market 1, (5) the number of Left-hand Certificates you would like to 

redeem in Market 2, and, finally, (6) the number of Right-hand Certificates you would like to 

redeem in Market 2. 

In the example indicated below, a participant may choose to buy two Left-hand Certificates for a 

total price of 60 francs by circling the option “2 for 60 francs” in the column labeled “Left-hand” 

under the caption “How many certificates will you buy?” The participant may buy four Righthand Certificates by circling the option “4 for 280 francs” in the column labeled “Right-hand” 

under the caption “How many certificates will you buy?” Similarly, under the captions 

“MARKET 1” and “MARKET 2,” you will indicate how many Left-hand and Right-hand 

Certificates you would like to redeem in the two markets. 

Participant Number: Market Period: 

(Circle One) (Circle One) (Circle One) (Circle One) (Circle One) (Circle One)

Left-hand Right-hand Left-hand Right-hand Left-hand Right-hand

0 0 for 0 0 for 0 0 for a fee of 0 for a fee of 0 for a fee of 0 for a fee of

francs francs 0 francs 0 francs 0 francs 0 francs

1 1 for 30 1 for 70 1 for a fee of 1 for a fee of 1 for a fee of 1 for a fee of

francs francs 50 francs 10 francs 50 francs 10 francs

2 2 for 60 2 for 140 2 for a fee of 2 for a fee of 2 for a fee of 2 for a fee of

francs francs 100 francs 20 francs 100 francs 20 francs

3 3 for 90 3 for 210 3 for a fee of 3 for a fee of 3 for a fee of 3 for a fee of

francs francs 150 francs 30 francs 150 francs 30 francs

4 4 for 120 4 for 280 4 for a fee of 4 for a fee of 4 for a fee of 4 for a fee of

francs francs 200 francs 40 francs 200 francs 40 francs

.. . . . .

.. . . . .

.. . . . .

15 15 for 450 15 for 1050 15 for a fee of 15 for a fee of 15 for a fee of 15 for a fee of

francs francs 750 francs 150 francs 750 francs 150 francs

16 16 for 480 16 for 1120 16 for a fee of 16 for a fee of 16 for a fee of 16 for a fee of

francs francs 800 francs 160 francs 800 francs 160 francs

How many certificates

will you buy?

MARKET 1:

How many certificates will you 

redeem in Market 1?

MARKET 2:

How many certificates will you 

redeem in Market 2?

CERTIFICATE REDEMPTION FORM
46

Given a participant chooses to buy two Left-hand Certificates, he or she can redeem as many as 

two Left-hand Certificates, but not more than two Left-hand Certificates in Market 1 and in 

Market 2. In the example indicated above, the participant has chosen to redeem one Left-hand 

Certificate in Market 1 for a fee of 50 francs and two Left-hand Certificates in Market 2 for a fee 

of 100 francs. Altogether the participant used two Left-hand Certificates to sell three units. 

Similarly, the decision to buy four Right-hand Certificates enables the participant to redeem as 

many as four Right-hand Certificates in Market 1 and in Market 2. In the example indicated 

above, the participant has chosen to redeem four Right-hand Certificates in Market 1 for a fee of 

40 francs and to redeem three Right-hand Certificates in Market 2 for a fee of 30 francs. 

Altogether, the participant used the four Right-hand Certificates to sell seven units. 

Each participant will make his or her selections secretly. The experimenter will collect all 

participants’ forms, determine the numbers of Left-hand and Right-hand Certificates redeemed in 

Markets 1 and 2, and find the corresponding prices on the Schedules of Redemption Values. The 

experimenter will publicly report the prices at which the certificates will be redeemed and will 

report the total number of Left-hand Certificates and Right-hand Certificates redeemed in each 

market. 

The experimenter will return each participant’s Certificate Redemption Form, and participants 

will calculate their earnings for the Market Period on an Accounting Sheet. (See below.) 

On this sheet the participant will record the francs they used to buy certificates and the francs 

they used to pay redemption fees. The sum of purchases and redemption fees constitutes “Total 

Expenses” for the Market Period. 

Participants will also use this sheet to record the revenues they gained from selling units on 

Markets 1 and 2. You calculate your revenue from a market by multiplying the sum of all Lefthand and Right-Certificates you redeemed in that market by the Redemption Value that obtained 

in that market. “Total Revenue” is comprised of the revenue gained from both markets. 

Finally, you will calculate the payoff you earned on francs you did not use to buy certificates. 

Your total income, then, will be comprised of your Total Revenue from the sale of units, the 

payoff you earned on francs not used to buy certificates, and the francs you did not use. The 

payoff you gain from the entire Market Period is the difference between your total income and 

your total expenses. 

Participants will use one Certificate Redemption Form and one Accounting Sheet for each 

Market Period. At the end of the experiment, you will add up the payoffs from each of the 

Market Periods. The resulting sum will constitute your total payoff for participating in the 

experiment. The experimenter will convert your francs into dollars at a rate of 2,000 francs to 

the dollar. 

You will not be able to apply payoffs earned in previous Market Periods to your participation in 

succeeding Market Periods. In each Market Period your participation will depend only on the 

1,000 francs the experimenter gives you. 
47

Participant Number: Market Period: 

EXPENSES

Purchases (francs)

Total

Left-hand Right-hand Purchases

Certificates Certificates (Less than 1001 francs)

Redemption Fees (francs)

Left-hand Right-hand Total

Certificates Certificates Redemption Fees

Market 1 Market 1 Total :

Market 2 Market 2 Total:

TOTAL EXPENSES:

REVENUE

Left-hand Right-Hand

Total Number

Redeemed

Redemption 

Values (francs)

Total Income

(francs)

Market 1 X

Market 2 X

TOTAL REVENUE from the REDEMPTION OF CERTIFICATES:

PROFIT from the Redemption of Certificates = REVENUE minus EXPENSES:

Francs not used to purchase certificates x 1.2:

Payoff = PROFIT plus (Unused FRANCS x 1.2):

Number of 

Certificates Redeemed

ACCOUNTING SHEET