Can Blockchain Solve the Hold-up Problem in Contracts?

A vexing problem in contract law is modification. Two parties sign a contract but before they fully perform, they modify the contract. Should courts enforce the modified agreement? A private remedy is for the parties to write a contract that is robust to hold-up or that makes the facts relevant to modification verifiable. Provisions accomplishing these ends are renegotiation-design and revelation mechanisms. But implementing them requires commitment power. Conventional contract technologies to ensure commitment – liquidated damages – are disfavored by courts and themselves subject to renegotiation. Smart contracts written on blockchain ledgers offer a solution. We explain the basic economics and legal relevance of these technologies, and we argue that they can implement liquidated damages without courts. We address the hurdles courts may impose to use of smart contracts on blockchain and show that sophisticated parties' ex ante commitment to them may lead courts to allow their use as pre-commitment devices.

Appointments: A More Effective Commitment Device for Health Behaviors

Health behaviors are plagued by self-control problems, and commitment devices are frequently proposed as a solution. We show that a simple alternative works even better: appointments. We randomly offer HIV testing appointments and financial commitment devices to high-risk men in Malawi. Appointments are much more effective than financial commitment devices, more than doubling testing rates. In contrast, most men who take up financial commitment devices lose their investments. Appointments address procrastination without the potential drawback of commitment failure, and also address limited memory problems. Appointments have the potential to increase demand for healthcare in the developing world.

Allies or Commitment Devices? A Model of Appointments to the Federal Reserve

We present a model of executive-legislative bargaining over appointments to independent cen-tral banks in the face of an uncertain economy with strategic economic actors. The model highlights the contrast between two idealized views of Federal Reserve appointments. In one view, politicians prefer to appoint conservatively biased central bankers to overcome credible commitment problems that arise in monetary policy. In the other, politicians prefer to appoint allies, and appointments are well described by the spatial model used to describe appointments to other agencies. Both ideals are limiting cases of our model, which depend on the level of economic uncertainty. When economic uncertainty is extremely low, politicians prefer very conservative appointments. When economic uncertainty increases, politicians’ prefer central bank appointees closer to their own ideal points. In the typical case, the results are somewhere in between: equilibrium appointments move in the direction of politician’s preferences but with a moderate conservative bias.

Testing Tripwire Theory Using Survey Experiments

Many scholars and policymakers assume that attacks on forward deployed U.S. troops—“tripwires”—will prompt strong domestic political support for escalation against the attacker. This conjecture informs policy and has deep theoretical roots, yet it is undertheorized and largely untested. We identify and develop two theoretical mechanisms – reputation and revenge – capable of explaining why such attacks might prompt support for escalation, even though prior research shows that casualties suffered during a conflict reduce support for intervention. We then use two survey experiments to examine whether and how attacks influence Americans’ support for intervention. We find that hypothetical attacks on contingents of troops deployed overseas increase support for escalation only modestly and in ways that better reflect demands for revenge rather than concerns about reputation. Our findings imply that confident assessments that forward deployed troops serve as strong pre-commitment devices need to be tempered, pending further theoretical and empirical analysis.

On the Value of Penalties in Time-Inconsistent Planning

People tend to behave inconsistently over time due to an inherent present bias. As this may impair performance, social and economic settings need to be adapted accordingly. Common tools to reduce the impact of time-inconsistent behavior are penalties and prohibition. Such tools are called commitment devices. In recent work Kleinberg and Oren [6, 7] connect the design of prohibition-based commitment devices to a combinatorial problem in which edges are removed from a task graph G with n nodes. However, this problem is NP-hard to approximate within a ratio less than √n/3 [2]. To address this issue, we propose a penalty-based commitment device that does not delete edges but raises their cost. The benefits of our approach are twofold. On the conceptual side, we show that penalties are up to 1/β times more efficient than prohibition, where β ϵ (0,1] parameterizes the present bias. On the computational side, we significantly improve approximability by presenting a 2-approximation algorithm for allocating the penalties. To complement this result, we prove that optimal penalties are NP-hard to approximate within a ratio of 1.08192.

Commitment Devices for Health

This chapter examines how and why commitment devices have been used for weight management and frames research priorities going forward. A theoretical framework drawing on Thaler and Shefrin motivates the use of commitment devices to change health behaviours. An original taxonomy separates commitment devices into three distinct types. A review of the empirical literature, with a focus on unexpected findings that defy theoretical predictions, indicates that commitment devices (1) can have positive effects on health behaviours, but (2) can also have unintended effects, which warrants further research attention to under-theorised issues of ‘commitment overload' and ‘moral licensing', and empirical testing of online commitment strategies. The COVID-19 pandemic emphasises the need for innovative but evidence-based digital health interventions. The chapter closes with suggestions for policymakers considering commitment devices for preventative health behaviours.