Setup

• Tend to get inefficiency when we consider interactions between uninformed and informed parties:
  • Sender-receiver
  • Market for lemons
  • Bargaining
• In some of these settings, delegating decision power to the informed party:
  • Potential for efficient solution
  • But share of surplus created is smaller for the party ceding power 

Motivation

Constrained Delegation allows for an intermediate outcome: 

• The uninformed party delegates some of their decision power to the informed party
• But imposes some constraints on the choices the informed party can make
  • For example, minimum and maximum limits

Literature

More recently: Kartik, Kleiner, Van Weelden (2021)

Take a mechanism design approach to show when Proposer:

• Fully delegates
• Makes a take-it-or-leave-it offer
• Offers a limited set of offers

Theory

Vetoer

0

1

Proposer

\( \theta \)

• Proposer has:
  • Increasing payoff \( \pi (z) \) over outcome \( z \)
    • \(\pi(z)\) linearly increasing in experiment
  • Makes an offer \(X\subset\left[0,1\right]\) to the vetoer
• Veteor has:
  • Private information: ideal point \(\theta \) with \( \theta \sim F(\theta) \), preference with convex loss in \(\left|z-\theta\right|\) 
  • Veto point given by \(0\), chooses \(z\in \left\{0\right\}\cup X \)

Theory

0

1

Proposer

• Vetoer's realized preference can be...
  • more aligned with Proposer (\(\theta\) closer to 1)

Theory

0

1

Proposer

• Vetoer's realized preference can be...
  • more aligned with Proposer (\(\theta\) closer to 1)
  • or less aligned  (\(\theta\) closer to 0)

Theory

0

1

Proposer

Key experimental/theoretical manipulation for Proposer:

1. Take it or leave it offer \(X=\left\{ x\right\}\)
2. Delegation of multiple choices to vetoer \(X\subset\left[0,1\right]\)
   • Interval optimal choice, so \(X=\left[\underline{x},\overline{x}\right]\)

Theory: Take it or Leave It

0

1

Proposer

Offer \( x \)

Theory: Take it or Leave It

0

1

Proposer

Offer \( x \)

\( \frac{ x}{2} \)

Suboptimal:

inefficient as \(\theta\)

preferred by both

Breakdown:

inefficient as \( \theta\) preferred by both

Theory: TIOLI equilibrium

• Marginal increase in offer \(x\) leads to:
  • Marginal gain of \(\pi'(x)\cdot(1-F(\tfrac{x}{2}))\)
  • Marginal loss of \(\left(\pi(x)-\pi(0)\right)\cdot \tfrac{1}{2}f(\tfrac{x}{2})\)

\(z=0\)

\(z=x\)

Theory: TIOLI equilibrium

• Marginal increase in offer \(x\) leads to:
  • Marginal gain of \((1-F(\tfrac{x}{2}))\)
  • Marginal loss of \(\tfrac{x}{2}\cdot f(\tfrac{x}{2})\)

\(z=0\)

\(z=x\)

Theory: Delegation

0

\( \theta \)

1

Vetoer ideal

Proposer

• Proposer delegates a set of options \(X\)
• Vetoer chooses \(z\in\left\{0\right\}\cup X\)
  • either the veto threat point (here \(0\))
  • or some offer in delegation set

Theory: Delegation

0

1

Offer \( [x,1] \)

\( x\)

Theory: Delegation

0

1

\( \frac{ x}{2} \)

Offer \( [x,1] \)

\(x \)

No suboptimal deals:

The delegation mechanism ensures

these options are available

Breakdown:

inefficient as \( \theta > 0 \)

Theory: Delegation Equilibrium

• Marginal increase in lower offer \(x\) leads to:
  • Marginal gain of \((F(x)-F(\tfrac{x}{2}))\)
  • Marginal loss of \(\tfrac{x}{2}\cdot f(\tfrac{x}{2})\)

Theory: Delegation Equilibrium

• With increasing density we get a corner solution \(X=\left\{1\right\}\):
  • coincides with TIOLI solution, \(x^\star=1\)

Offer \( [x,1] \)

Theory: Delegation Equilibrium

• With decreasing density we get the other corner solution:
  • Full delegation, with \(X^\star=\Theta\)
  • Results in first-best for responder with \(z^\star=\theta\)

Offer \( [x,1] \)

Theory: Summary

• Delegation should substantially reduce inefficiency
  • Direct Effect: Remove suboptimal deals by providing choice to informed party
  • Indirect Effect: Changing the Proposers' optimization margin leads to more latitude on lower limit, decreasing breakdown
• Delegation is valuable tool for Proposer under veto threat
• Proposers use of delegation shifts with expected alignment with Responder:
  • Increasing \(f(\theta)\): No delegation
  • Single peaked \(f(\theta)\): constrained delegation
  • Decreasing \(f(\theta)\): Full delegation

Theory: Environment Characteristics

• Delegation here is a compromise over veto threat:
  • Proposer has state-independent preferences
  • Tension here is over failure to agree
• This is not Delegation over information a la Holmstrom (1984)
  • Expert there has valuable information for Proposer's state-dependent preference
  • Tension is over expert's preference misalignment

Theory: Hypotheses

1. Delegation increases efficiency through:
   • Reduced breakdown
   • Reduced suboptimal deals
2. Delegation is a valuable tool to Proposers
3. Delegation moves with expected alignment:
   • Full delegation with decreasing density
   • No compromise with increasing density
   • Constrained with single-peaked density
4. Cheap talk is:
   • Partially Informative in TIOLI (Matthews QJE 1989)
   • Uninformative in Delegation (Kartik et al AER 2021)

Experimental Design

• Construct environment that directly follows the veto-bargaining framework
• One challenge: how to bring this abstract environment to a participants in a way that is easier to understand?

Experimental Design

• Construct environment that directly follows the veto-bargaining framework
• One challenge: how to bring this abstract environment to a participants in a way that is easier to understand?

Experimental Design: Framing

Proposer

Seller

Decreasing

probability

Inverse-U shaped

Increasing probability

Experimental Design

• Within-subject variation:
  • Varying distributions (high, middle, low) for the Responder  
  • Changing roles: 5 rounds in one role, 5 rounds in the other, and back to first role for 5 more rounds
• Between subject 2 x 2: 

Experimental Design

Collect other behavioral variables:                                                 

• Robot Responder: remove strategic uncertainty/other regarding
• Pure incentives: present mechanism choice as lottery (remove mechanism complexity)
• Pure allocation: remove uncertainty and mechanism

Addendum collection (70 more subjects in NoChat):

• No veto: Remove responder's veto threat
• Expected Value: remove risk and other regarding
• CF Mechanism Value: Elicit WTP for the Delegation/TIOLI

Low

Middle

High

Sanity check: Proposers respond to alignment

Sanity check: Proposers respond to alignment

Model of Responder Behavior

• Large majority (~90%) of Responder decisions are selfish/rational
• The remaining 10% are inefficient, but tend to choose more generously for Proposers
• Estimate a model of Responder behavior (which we use to integrate out the exogenous shock \(\theta\) revealed after Proposer decision

Model of Buyer Behavior

• Fehr-Schmidt parameterized logit model for Resp. behavior:
  \(U_R(x_R,x_P)=\lambda \cdot x_R - \alpha\cdot \max(x_P-x_R,0) - \beta\cdot \max(x_R- x_P ,0) \)

Here express relative parameters:

• Relative effect of disadvantageous inequality: \(\tfrac{\alpha}{\lambda}\) 
• Relative effect of advantageous inequality:\(\tfrac{\beta}{\lambda}\)

Efficiency of Outcomes

Take it or Leave It

Delegation

Delegation does increase efficiency

Result 1:

Conditional Delegation drastically increases efficiency in our experiments relative to Take-it-or-leave-it offer.

Distribution of Outcome

Who benefits from delegation? 

Decreasing

Density

Who benefits from delegation?

Single Peaked

density

Who benefits from delegation?

Increasing

density

Result 2:

While Proposers do benefit, efficiency gains generated by Delegation are mostly captured by the Responder.

What would happen if...

Force Buyers to be selfish maximizers?

Force Proposer to max-out top of interval

What explains behavioral deviations?

Additional experimental treatments:

1. Replace Responder with a payoff-maximizing Robot player
   • Remove strategic uncertainty and other-regarding concerns
2. Maintain Robot; remove mechanism framing
   • Removes complexity, pure choice over lotteries
3. Pure other-regarding decision
   • Remove risk, purely distributive decision
4. Remove veto threat
   • Assess effect of deliberation in dictator analog
5. Pay with expectation of decision
   • Remove risk maintain strategic uncertainty
6. Counterfactual assessment
   • Assess behavior and valuation for Alt. Mechanism with experience of other mechanism

Robot Buyer

• Same decision screen/interface
• Same payoffs
• Repeat for all three urns

Remove mechanism framing

• Choices as a lottery
• Each lottery option corresponds to  delegation decision
  • Do this for all three urns
  • TIOLI and Delegation

Self/Other Distribution Choice

• Fix state, look at dictator problem for Seller under complete information
• Repeat this for low/med/high states

What explains behavioral deviations?

Proposer failure with delegation not driven by other regarding as when we replace the Buyer with a Robot observed choices closely mirror delegation choices

What explains behavioral deviations?

However, when we remove the mechanism framing,  lottery choices indicate movement toward less latitude in delegation

Replace delegation decisions with lottery?

Replace delegation decisions with lottery?

• Sellers do better with Delegation when presented as pure lottery
  • Esp. with more alignment with Responder
• Suggests optimization failure over their use of the mechanism

How is delegation affected by veto?

• Can remove the veto threat point to examine value of delegation absent the veto threat
• Analagous to comparing Ultimatum vs Dictator game:
  • With uncertainty over Responder preference
  • Selfish outcome unaffected by Delegation v. TIOLI

Delegation with No Veto

Without veto-threat, having the delegation option reduces the Proposer's payoff

Is Delegation valued by Proposers

1. Gain Experience: Subjects complete 15 rounds under fixed rule
2. Remove risk: Asked to make a Proposer decision paid with the average behavior across many Robots buyers trained to mirror prior subjects
3. Counterfactual: Explain alternative CF rule, make decision paid with average across Robot buyers trained to CF
4. Assess relative value: Ask about preference over payment in Parts 2 and Parts 3 where we add/subtract money.

Perceived Value of Delegation

Results: Key Points

• Proposers respond to the expected alignment
  • Offer more latitude than theory point prediction
  • Delegation offers even more latitude
• Delegation mechanism substantially increases efficiency
  • This is not true without the veto mechanic
• Efficiency gains not shared equally:
  • Responder gets the lion's share
  • Value to Proposer well ordered by theory
    • This is also true for their expectations over the CF
  • Proposers give away some of their share through optimization failures
    • But this affects distribution rather than efficiency

Pre-play Communication

Theory: Communication

• For TIOLI, partially informative
  • Two vague messages
  • High/Low

• For Delegation, no gain predicted when using interval
  • Only useful for No Compromise outcome (High urn)

Results: Chat

• TIOLI, \(\theta=3\), Middle urn

Results: Chat

• Delegation, \(\theta=3\), Middle urn

Results: Chat

• Delegation, \(\theta=2\), Low urn

Results: Inefficiency (Data with No Comm)

TIOLI

Delegation

Results: Inefficiency (Data with Comm)

Results: Communication

• Still a lot of work to do with the communication data
• Cannot integrate out Responder state realization as conversation before Proposer choice now
• We use LLM to code the chats:
  • Across treatments Responders reveal their ideal:
    • 76% in TIOLI (no effect by \(\theta\))
    • 52% in Delegation (decreasing in \(\theta\))
  • Lies are to claim lower values of \(\theta\)
    • False claims in Delegation are much lower than TIOLI
  • More breakdown in Delegation with chat when Responders honestly reveal 

Results: Prop. Outcome (relative to full deleg)

Diagnosing the Failures: Pure Optimization

Diagnosing the Failures: Lotteries

Diagnosing the Failures: Distribution