Journal of Economic Interaction and Coordination最新文献

This paper introduces a new storable vote mechanism (Storable Votes-Pay as you win, SV-PAYW) where a fixed number of votes is cast among different alternatives, and the votes spent (and redistributed) on each election depend only on the number cast for the winning alternative. The mechanism is expected to deliver more enfranchisement, efficiency and a reduction of uncertainty and strategic behavior with respect to previously known voting systems. To compare the pure storable votes with the SV-PAYW implementations, two key characteristics are monitored: the “enfranchisement gap”, which measures the proportionality between political influence and electoral victories, and the “efficiency ratio”, which assesses the utility derived from the allocation of electoral victories on a scale from random allocation (zero) to the social optimum (one). SV-PAYW consistently outperforms pure storable votes in terms of enfranchisement in all cases. Additionally, as a general rule (there are some exceptions), the “efficiency ratio” tends to be higher for SV-PAYW, hovering around 0.7.

Abstract

The growing competition in the retail market pushes retailers to optimize their sales and marketing strategies. In particular, in sectors where the profit margins are more restricted and customer loyalty depends heavily on the prices offered, in fact, understanding consumer reactions to sales promotions and providing them with the right deal at the right time is critical for retailers to survive. In this study, we propose an agent-based model that uses the Belief Desire Intention (BDI) concept to model how customers react to the various promotional offers they receive, and our study involves a seller agent that dynamically learns the appropriate promotion to give these customers on a customer-specific basis. Using empirical research findings in the literature, we developed the BDI part of the model based on the “Big Five-Factor Model”. Apart from this, we used the Q-learning algorithm to train the seller agent. Furthermore, one of our goals in this study is to show that learning-based decision-making agents can be more competitive on the market than rule-based decision-making agents. We therefore added a rule-based agent to the model and compared its efficacy to that of the learning-based decision-making agent. The model was tested in an artificial market through a series of experiments. The experiment results show that the proposed model can be used in actual applications to automate sales promotion decisions.

Open innovation ecosystems play an important role in corporate and product innovation and contribute to the success of economic development at the municipal level, yet there is little research on the factors driving innovative behavior within a municipal innovation ecosystem (MIE). This study explores how the interpersonal and intrapersonal resources of the individuals involved contribute to innovative behavior within a city’s innovation ecosystem. This study applies a mixed-method approach to build a novel framework to examine innovative behavior in the Mobile, Alabama, municipality. A moderated mediation model is tested using PLS-SEM. The qualitative findings presented in this study uncover new constructs useful for assessing innovation at the individual level in a municipality. Scale development steps are employed to validate these new constructs. The SEM results suggest that personal resources and social interactions impact innovative behavior in an MIE.

Public sector organisations are increasingly tasked to adapt and evolve, dealing with shocks and disturbances without compromising their role as wealth generators. However, as complex adaptive systems, they do not manifest equal capacity for adaptation and evolution in response to complexity. This article explores the role of resilience in public sector organisations and sheds light on the interactions between different elements of resilience that enable these organisations to navigate complexity. The study combines a systematic literature review and a quantitative empirical investigation to propose and test a framework of organisational resilience in the public sector. It identifies planning, adaptation, and enhanced learning as critical phases to explain organisational resilience and how they intervene dynamically when dealing with adversity. The empirical investigation analyses the Lithuanian public sector organisations and explains the relationships among the model dimensions. The paper offers a model for theory development to understand the dimensions of the resilience of complex adaptive public sector organisations and how they interrelate with each other. For practice, it suggests which aspects are relevant to help public sector organisations develop their capacity to build resilience and overcome adversity.

New technology-based firms (NTBFs) present high levels of information asymmetry since knowledge base, the intangibility of assets, and appropriability issues are particularly pronounced. Investors experiment with difficulties in evaluating the quality of NTBFs and look into insights representing the venture's promises and outlooks, and NTBFs need to face limited financing opportunities. Therefore, information asymmetries and moral hazards may influence the financing system of NTBFs. Based on the signaling theory, we propose a configurative approach built on five accessible signals (founders’ education and work experiences, property rights, alliances, and size) to identify combinations of signals conducive to the NTBF reaching a first financial round. By adopting a configurative approach through the qualitative comparative analysis, we advance our understanding of the intricate dynamics between NTBFs and investors, shedding light on the complexity and interplay of various factors influencing the financing outcomes.

Abstract

The monetary policy operations of a central bank (CB) involve allocation decisions when purchasing assets and taking collateral. A green monetary policy aims to steer or tilt the allocation of assets and collateral toward low-carbon industries, to reduce the cost of capital for these sectors in comparison to high-carbon ones. Starting from a corporate bonds purchase program (e.g., CSPP) that follows a carbon-neutral monetary policy, we analyze how a shift in the CB portfolio allocation toward bonds issued by low-carbon companies can favor green firms in the market. Relying on optimal portfolio theory, we study how the CB might include the risk related to the environmental sustainability of firms in its balance sheet. In addition, we analyze the interactions between the neutral or green CB re-balancing policy and the evolutionary choice (i.e., by means of exponential replicator dynamics) of a population of firms that can decide to be green or not according to bonds borrowing cost.

Recent trends in the market for corporate bonds -such as its increase in size, the significant presence of low-quality debt instruments and the trading activity of non-financial corporations- call for the need of an investigation of the possible implications on the real economy. Although the phenomenon is still minor, the present paper aims at giving some early insights concerning the introduction of a corporate bond market, where also nonfinancial firms can invest. To do so, we build on an existing macroeconomic agent-based model of the CATS tradition and we introduce the possibility for firms to raise money through an alternative credit channel, i.e., the corporate bond market- and the opportunity for households and firms to invest part of their financial wealth into a portfolio of Government bonds and corporate bonds. Experiments based on single runs or on 100 Monte Carlo simulations suggest that the introduction of a bond market may exacerbate existing crisis and recessions and that when firms buy bonds, inequality may escalate. To the best of our knowledge, this paper constitutes one of the first attempts to incorporate a corporate bond market into a macro ABM.

Inspired by macroeconomic scenarios, we aim to experimentally investigate the evolution of short- and long-run expectations under different specifications of the fundamentals. We collect individual predictions for future prices in a series of Learning to Forecast Experiments with a time-varying fundamental value. In particular, we observe how expectations evolve in markets where the fundamental value follows either a V-shaped or an inverse V-shaped pattern. These conditions are compared with markets characterized by a constant and a slightly linear increasing fundamental value. We assess whether minor but systematic variations in the fundamentals affect individual short- and long-run expectations by considering positive and negative feedback-expectation systems. Compared to a setting with constant fundamentals, the slowly varying fundamentals have a limited impact on how subjects form their expectations in positive feedback markets, whereas in negative feedback markets we observe notable changes.