ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

This paper examines knapsack auctions as a method to solve the knapsack problem with incomplete information, where object values are private and sizes are public. We analyze three auction types-uniform price (UP), discriminatory price (DP), and generalized second price (GSP)-to determine efficient resource allocation in these settings. Using a Greedy algorithm for allocating objects, we analyze bidding behavior, revenue and efficiency of these three auctions using theory, lab experiments, and AI-enriched simulations. Our results suggest that the uniform-price auction has the highest level of truthful bidding and efficiency while the discriminatory price and the generalized second-price auctions are superior in terms of revenue generation. This study not only deepens the understanding of auction-based approaches to NP-hard problems but also provides practical insights for market design.

This study provides a four-good general equilibrium framework with international trade for assessing the impact of the advancement of automation and artificial intelligence (A&AI) on the welfare of a group of agents who are excluded from both owning productive assets, such as capital and land, as well as consuming digital output (digital exclusion). We show that, depending on the magnitude of the factor intensities, the accumulation of A&AI capital may negatively affect the income of the excluded group, who provide unskilled labor, or the owners of land. In doing so, we bring out the conflict of interests that may arise between the owners of A&AI capital and other groups within society, which has implications for the pressure that exists to slow down the adoption of A&AI in an economy.

In this paper, we examine the usefulness of time commitment as a voting resource for decentralized governance when the identity of voters cannot be verified. In order to do so, we take a closer look at two issues that confront token-based voting systems used by blockchain communities and organizations: voter fraud through the creation of multiple identities (Sybil attack) and concentration of voting power in the hands of the wealthy (plutocracy). Our contribution is threefold: first, we lay analytical foundations for the formal modeling of the necessary and sufficient conditions for a voting system to be resistant to a Sybil attack; second, we show that tokens as the only instrument for weighting votes cannot simultaneously achieve resistance to both Sybil attacks and a plutocracy in the voting process; and third, we design a voting mechanism, bond voting, that is Sybil resistant and offers a second instrument (time commitment) that is effective for countering plutocracy when large token holders also have a relatively high opportunity cost of locking tokens for a vote. Overall, our paper emphasizes the importance of time-based suffrage in decentralized governance. This paper was accepted by Joshua Gans, business strategy. Funding: This work was supported by Gitcoin (Using commitment voting for better DAO governance) and the Australian Research Council [Grant DP200101808].

In the Knapsack Problem a set of indivisible objects, each with different values and sizes, must be packed into a fixed-size knapsack to maximize the total value. The knapsack problem is known to be an NP-hard problem even when there is full information regarding values and sizes. In many real-world situations, however, the values of objects are private information, which adds another dimension of complexity. In this paper we examine the knapsack problem with private information by investigating three practical auctions as possible candidates for payment rules in a setup where the knapsack owner sells the space to object owners via an auction. The three auctions are the discriminatory price, the generalized second-price and the uniform-price auctions. Using a Greedy algorithm for allocating objects, we analyze bidding behavior, revenue and efficiency of these three auctions using theory, lab experiments, and AI-enriched simulations. Our results suggest that the uniform-price auction has the highest level of truthful bidding and efficiency while the discriminatory price and the generalized second-price auctions are superior in terms of revenue generation.

In this paper, we take a close look at a problem labeled maximal extractable value (MEV), which arises in a blockchain due to the ability of a block producer to manipulate the order of transactions within a block. Indeed, blockchains such as Ethereum have spent considerable resources addressing this issue and have redesigned the block production process to account for MEV. This paper provides an overview of the MEV problem and tracks how Ethereum has adapted to its presence. A vital aspect of the block building exercise is that it is a variant of the knapsack problem. Consequently, this paper highlights the role of designing auctions to fill a knapsack--or knapsack auctions--in alleviating the MEV problem. Overall, this paper presents a survey of the main issues and an accessible primer for researchers and students wishing to explore the economics of block building and MEV further.

We examine the relationship between research and development (R&D) expenditures and the expected impact of diseases in a simple theoretical framework that allows for intellectual property rights (IPR) protection to be strong or weak. In our theoretical model, an agent forms an expectation of the impact of a disease using a publicly available statistic on the (population level) disease burden, such as disability-adjusted life year. We show that a profit-maximising firm will exert relatively more R&D effort on diseases with intermediate expected impacts. We also discuss how a weak IPR regime alters the pattern of R&D investment.

Using the production function suggested by Jones and Manuelli (1990) , this article explores the consequences of introducing automation and artificial intelligence (A&AI) into a trade theoretic framework. An immediate implication is the possibility of a reversal of the trade patterns predicted by standard Heckscher–Ohlin theory, leading to Leontief paradox-type outcomes. We show that the Jones–Manuelli production function is capable of generating factor intensity reversals; consequently, our analysis suggests that factor intensity reversals may have a more prominent role to play in trade theory in the future when AI becomes prevalent. JEL: F10, O30

Two of the most important technological advancements currently underway are the advent of quantum technologies, and the transitioning of global financial systems towards cryptographic assets, notably blockchain-based cryptocurrencies and smart contracts. There is, however, an important interplay between the two, given that, in due course, quantum technology will have the ability to directly compromise the cryptographic foundations of blockchain. We explore this complex interplay by building financial models for quantum failure in various scenarios, including pricing quantum risk premiums. We call this quantum crypto-economics.

Non-fungible tokens (NFTs) on blockchains have recently emerged as a means of certifying the originality of digital properties, such as artwork. In this paper, we examine limited-edition auctions for the sale of digital artwork using NFTs. We study two types of limited-edition auctions that have been used in practice: the `silent' and `ranked' auctions. We argue that the silent limited-edition auction as currently used in NFT markets is a variant of the well-known discriminatory price auction. We derive the Bayesian Nash equilibrium of this auction, and show that it is revenue equivalent to a VCG auction. Our analysis suggests that bidding behavior in a silent limited-edition auction is more aggressive than a standard discriminatory price auction without editioning; consequently, equilibrium bids are higher in the former. We also study the generalized English auction as an outcome equivalent auction to the ranked limited-edition auction, and show that this does not have a truthful equilibrium. Finally, we examine the uniform-price auction as a potential alternative mechanism for conducting an auction with editioning, and establish the absence of a truthful equilibrium in this instance as well. Our paper represents one of the first attempts to formally model the allocation of property rights using auctions in the digital environment, which is at the forefront of current innovation.

During a pandemic or other disaster, public visibility of the supply chain can be useful for controlling the symptoms of coordination failure, such as panic and hoarding, that arise from the desire for quantity assurance by various sectors of the economy. It is also important for efficient coordination of the logistics required to tackle the disaster itself, with vital information flows to centralized agencies leading the response as well as to decentralized agents upstream and downstream in a supply chain. Publicly visible information about the supply chain at the time of a crisis needs to be secure, timely, possibly selective in terms of access and the nature of information, and often anonymous. Recent advances in distributed ledger technology allow for these characteristics to be met. Building digital infrastructure that permits visibility of the supply chain when needed (even if dormant during normal times) is essential for economies to be more resilient to black swan events.

The standard economic model of intellectual property is an efficient property rights solution to a market failure problem of investment in a non-rival and non-excludable good. We propose an exchange theory of intellectual property based on a contracting approach in the context of market-making and enforcement of economic rights in exchange for monopoly taxation rights. We use a Hotelling (1929) type spatial model to show the relationship between location and pricing decisions of innovating firms under differing intellectual property, institutional quality, and taxation regimes.

Current incentives for publishing in academic journals result in a "winner-take-all" contest-like situation, with significant benefits for publishing research in quality journals. At the same time, empirically, we observe a greater incidence of research misconduct. The purpose of this paper is to summarize the nature and extent of the misconduct problem, to show why it may persist in the absence of conscious remedial action, and to discuss solutions that help lower the likelihood of spurious research escaping undetected. A simple model is constructed to emphasize that there exists the potential for a Prisoners' Dilemma in academia, where scholars engage in misconduct at equilibrium (the Academic Dilemma). The paper then examines why conventional "centralized" regulatory solutions under the current system are not likely to succeed in resolving the problem, analyzes the properties of a decentralized solution utilizing blockchains, and argues that once incentive structures in academia are factored in, a permissioned blockchain may emerge as an effective middle-ground solution for mitigating scientific misconduct. In doing so, the paper highlights the importance of new technologies and recent advancements in Open Science for battling misconduct, and takes stock of the evolving nature of academic publishing.

This paper examines the impact of luxury goods consumption by the wealthy on the welfare of socially excluded groups. We find that a deterioration in the luxury goods terms‐of‐trade or an increase in the capital used to produce nonluxury traded goods have the consequence of increasing the welfare of the wealthier sections of society at the expense of the socially excluded groups.

We present an analytical approach to institutional analysis that draws inspiration from control process engineering in the physical sciences. We characterize smart institutions as having three foundational features. First, smart institutions are context sensitive and expressly allow for a unified consideration of social, political and economic factors, thereby providing a richer and more eclectic approach to their operation. Second, smart institutions are forward-looking in their operation rather than deriving from their past functions and purpose in contrast to most generic institutions. Third, as opposed to generic institutions, smart institutions emphasize the role of information and specifically that of subjective social feedback on institutional performance. A theory of smart institutions, consequently, presents distinct advantages over traditional institutional analysis.

We examine the impact of regulation on the doping decisions of athletes in a Tullock contest. The regulatory measures we consider are greater monitoring by sports authorities and a lowering of the prize in the contest. When legal efforts and illegal drugs are substitutes, an increase in anti-doping regulation may, counterintuitively, increase the levels of doping activity by athletes. Anti-doping regulation can also have the undesirable consequence of decreasing legal efforts; in our model, this always occurs when legal efforts and illegal drugs are complements, and under certain circumstances when they are substitutes.

We build a model with two agents: domestic residents and temporary immigrants. The model incorporates Kaldorian disaggregation, with the two groups consuming different goods produced in the economy. It is established that, under certain conditions, an increase in immigrant labor lowers the welfare of the domestic residents. This runs against conventional wisdom that temporary immigration enhances the welfare of domestic residents.

In this paper we examine a four-good, four-factor model of trade with two agents: domestic residents and temporary migrants. This modelling framework has three important features: first, there are two tradable and two non-tradable goods; second, there exists Kaldorian disaggregation in consumption; third, the structure incorporates a combination of price adjustment. The results emphasize the influence of factor accumulation at constant traded goods prices on the variable prices of non-traded goods. We also analyse the impact of temporary migration and other structural parameters on domestic welfare. To highlight our results, our model is calibrated on a typical small open economy, Hong Kong, and a wide array of situations are presented when temporary migration and Kaldorian disaggregation can reduce domestic welfare in response to exogenous shocks.

We construct a four-good, four-factor general equilibrium model with trade to show that, under certain conditions, capital accumulation results in: (a) the immiserization of socially excluded groups; (b) an increase in the rate of return on capital; and (c) a decrease in the wage rate of socially excluded groups. Our analysis shows why social exclusion increases inequality.