Fixing feedback revision rules in online markets

Feedback withdrawal mechanisms in online markets aim to facilitate the resolution of conflicts during transactions. Yet, frequently used online feedback withdrawal rules are flawed and may backfire by inviting strategic transaction and feedback behavior. Our laboratory experiment shows how a small change in the design of feedback withdrawal rules, allowing unilateral rather than mutual withdrawal, can both reduce incentives for strategic gaming and improve coordination of expectations. This leads to less trading risk, more cooperation, and higher market efficiency.     

基于Arduino的购物小车的设计与实现

本文通过设计一个全自动运行的机器人,使用Arduino Mega2560作为核心控制硬件,去实现在空地中识别所要抓取的物体。能够处理各种未知的干扰物并成功将物品放到指定地点。本文运用Arduino和Python来进行开发购物小车的系统。     

Two steps forward,one step back? Quantifying the pecuniary costs of debt account aversion and the debt snowball

The interest-minimizing strategy to paying multiple debts is to make all minimum payments and allocate remaining funds to the debt with the highest interest rate. However, cognitive biases such as debt account aversion and financial advisors encourage borrowers to instead allocate remaining funds to debts with lower outstanding balances, a strategy known as the Debt Snowball. The author uses the 2016 Survey of Consumer Finances to quantify the pecuniary costs for American households of following the Debt Snowball and finds that the average household pays an additional 1.8%–4.3% in interest, leading to an aggregate transfer of wealth from borrowers to lenders of between $46.2 and $53.9 billion in excess of what would occur if borrowers instead minimized interest accrual. Due to differences in household debt structure, the Debt Snowball strategy imposes greater pecuniary penalties on low-income households, on Black households, and on households with more initial debts.     

Recent advances in reinforcement learning in finance

The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value- and policy-based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. We then discuss in detail the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising. Our survey concludes by pointing out a few possible future directions for research.     

Does income inequality really matter for credit booms?

This paper addresses the question whether income inequality is associated with credit booms, alongside other macroeconomic factors. We distinguish between the different types of credit booms—real estate credit booms, household credit booms, firm credit booms and credit booms that turn into crises. Furthermore, our analysis of a sample of 70 countries between 1990 and 2016 does not provide any evidence of credit booms driving income inequality. We observe that capital inflows increase the likelihood of credit boom occurrence, while countries experiencing high economic growth tend to have more credit booms. Finally, we note that credit booms are more frequent in countries with fixed exchange rate regimes.     

The impact of capital goods prices on Africa's economic performance

Over the last four decades, the relative price of investment goods in Africa has gone through a relatively large decrease, resulting in a steady convergence towards the levels recorded in high-income countries. This fact begs the following question: To what extent might the relative price decrease be a driving force behind the economic performance of this continent? The paper addresses this question from the perspective of a panel ARDL approach, using the Solow growth model—augmented with barriers to investment—as a framework. The results reveal that a one-unit decrease in the relative price of investment leads, in the long term, to a 4% increase in per capita GDP, an increase that could be neutralised by a 6.5 percentage points decrease in the savings rate. The findings contribute to the case for a policy mix that combines policies geared towards reducing investment distortions with those promoting savings mobilisation.     

Alacrity: a new model for venture acceleration

As research on venture accelerators develops, different models have emerged in the literature. These focus on the goals of the accelerator, which range from creating profit for managers and building support for business platforms to promoting regional economic development, as well as on its organizational form based on its for-profit or non-profit status. This article examines a novel model, the networked venture builder model, which offers an alternative perspective on the acceleration process. Using the example of the Alacrity Global Ecosystem (AGE), this article explores how the venture builder model includes characteristics of multiple accelerator types, which has helped it both rapidly grow new ventures and achieve substantial economic development goals. Synergies between the different aspects of the AGE’s organizational design help it support multiple missions. Drawing on interviews with key stakeholders and entrepreneurs within the AGE, this article describes the history of the AGE and its present form, providing new insights into a novel, but increasingly common, accelerator design and laying the basis for further research on its emerging organizational form.