Utility Research Articles

GDNF improves the cognitive ability of PD mice by promoting glycosylation and membrane distribution of DAT

The core of clinic treatment of Parkinson's disease (PD) is to enhance dopamine (DA) signaling within the brain. The regulation of dopamine transporter (DAT) is integral to this process. This study aims to explore the regulatory mechanism of glial cell line-derived neurotrophic factor (GDNF) on DAT, thereby gaining a profound understanding its potential value in treating PD. In this study, we investigated the effects of GDNF on both cellular and mouse models of PD, including the glycosylation and membrane transport of DAT detected by immunofluorescence and immunoblotting, DA signal measured by neurotransmitter fiber imaging technology, Golgi morphology observed by electron microscopic, as well as cognitive ability assessed by behavior tests. This study revealed that in animal trials, MPTP-induced Parkinson's Disease (PD) mice exhibited a marked decline in cognitive function. Utilizing ELISA and neurotransmitter fiber imaging techniques, we observed a decrease in dopamine levels and a significant reduction in the intensity of dopamine signal release in the Prefrontal Cortex (PFC) of PD mice induced by MPTP. Intriguingly, these alterations were reversed by Glial Cell Line-Derived Neurotrophic Factor (GDNF). In cellular experiments, following MPP + intervention, there was a decrease in Gly-DAT modification in both the cell membrane and cytoplasm, coupled with an increase in Nongly-DAT expression and aggregation of DAT within the cytoplasm. Conversely, GDNF augmented DAT glycosylation and facilitated its membrane transport in damaged dopaminergic neurons, concurrently reversing the effects of GRASP65 depletion and Golgi fragmentation, thereby reducing the accumulation of DAT in the Golgi apparatus. Furthermore, overexpression of GRASP65 enhanced DAT transport in PD cells and mice, while suppression of GRASP65 attenuated the efficacy of GDNF on DAT. Additionally, GDNF potentiated the reutilization of neurotransmitters by the PFC presynaptic membrane, boosting the effective release of dopamine following a single electrical stimulation, ultimately ameliorating the cognitive impairments in PD mice.Therefore, we propose that GDNF enhances the glycosylation and membrane trafficking of DAT by facilitating the re-aggregation of the Golgi apparatus, thereby amplifying the utilization of DA signals. This ultimately leads to the improvement of cognitive abilities in PD mouse models. Our study illuminates, from a novel angle, the beneficial role of GDNF in augmenting DA utilization and cognitive function in PD, providing fresh insights into its therapeutic potential.

An endogenous evolution mechanism model of asset prices based on time-varying risk aversion coefficient

In the traditional heterogeneous agent model, investors are assumed to be risk averse, and the wealth expected utility function maximization principle is used to form the optimal asset quantity demand. In such models, the risk aversion coefficient of investors is often assumed to be constant. This paper considers that the risk aversion coefficient of investors is time-varying and changes with the change of wealth, and establishes an endogenous evolutionary mechanism model formed by fundamental analysts, technical analysts, and market makers. Compared with the fixed risk aversion coefficient model, this paper analyzes the investor’s behavior, the interaction between investor behaviors, and the influence of different types of investors on the stability of the market. At the same time, we test asset price and asset behavior and conclude that investor behavior affects the stability of the system model. The numerical simulation of the corresponding stochastic model shows that the model can simulate the basic characteristics of financial time series, such as the partial peak and thick tail of asset return series, and the long memory of fluctuations.

Macroeconomic and trade policy impacts based on DSGE model

In the context of economic de-globalization, especially the impact of COVID-19, the adjustment of one country's macroeconomic and trade policies will have spillover effects on trading partners. Build DSGE models between the two countries, in which Epstein - Zin utility function replacement time can be divided into the utility function, to solve the model, using the second order approximate solution technology with particle filter to estimate the parameters, and analysis of the open economy under the background of a country's technological progress, monetary policy, labor supply, government spending and tariff policy on macroeconomic impact between the two countries. It is found that the impact of China's technological progress on F country's macro economy is greater than that of F country on China. The spillover effect of monetary policy on another country's macro economy will change the consumption structure of both countries. The impact of China's labor supply shock on the total investment of F country is greater than that of F country on China. The positive impact of government spending on another country's inflation rate is lower than other policy shocks. The impact of the positive impact of government expenditure in F country on China's aggregate demand is greater than that of China on F country.

Best Sales Selection Using a Combination of Reciprocal Rank Weighting Method and Multi-Attribute Utility Theory

The best salespeople are individuals who are not only able to meet or exceed sales targets, but also demonstrate exceptional skills in building relationships with customers, understanding their needs, and offering effective solutions. The problem of selecting the best salespeople often involves the challenge of an objective and fair assessment, as diverse evaluation criteria can affect the final result. One of the main obstacles is the presence of subjectivity in judgment, which can arise from personal preferences or pressure to maintain good relationships. This study aims to implement a sales performance evaluation model that combines the Reciprocal Rank Weighting and Multi-Attribute Utility Theory (MAUT) methods to obtain a more accurate and objective assessment of sales performance. This research contributes to the management literature and decision support systems by offering a new approach in sales performance evaluation. This opens up opportunities for further research and practical applications in the field of performance evaluation and salesforce management. Based on the final score calculated using the MAUT method, the salesperson rank from best to lowest is as follows: Sales 7 is ranked top with a value of 0.646, indicating the best overall performance. Sales 3 followed in second place with a value of 0.6125, followed by Sales 9 with a value of 0.5604 in third position.

Decision Support System for Determining the Best School Extracurricular Activities by Combining the ROC and MAUT Methods

The various extracurricular activities at school make students confused and difficult to choose which extracurricular activities are more suitable for participation. However, sometimes there are also students choosing extracurricular activities based on many of their friends. Therefore, determining the best school extracurricular activities is the best solution for students as a reference to find which is the best extracurricular activity. The criteria used in this study in choosing the best extracurricular activities are Regional Event Activities, Allocation, Creativity and Talent Channeling. By utilizing SPK, decision makers can make more systematic decisions, based on a deeper understanding of the various alternatives available and relevant criteria. SPK or decision support system is a technique that has the ability to determine a decision using a technical design based on alternatives and predetermined criteria. SPK or decision support system is a technique that has the ability to determine a decision using a technical design based on alternatives and predetermined criteria. In the context of extracurricular school selection, combining the ROC (Rank Order Centroid) and MAUT (Multi-Attribute Utility Theory) methods in a Decision Support System is an interesting approach. The ROC method is used to cluster and rank schools based on certain criteria, while MAUT helps in the calculation of appropriate weights for these criteria. By integrating these two methods, the SPK can provide a more structured guideline in the selection of extracurricular activities that suit students' interests and needs. The research results obtained show that the Futsal alternative is the first recommendation as the best extracurricular with a final value of 0.655086.

Implementasi Kombinasi Metode ROC dan MAUT Dalam Menentukan Aplikasi Chatting Terbaik

The rapid development of technology makes it easier for humans to communicate with each other more easily by using the best chatting applications. The purpose of this research is to help users who have just used a smartphone to choose the chatting application that suits their needs. The method used in this study is a Decision Support System with Rank Order Centroid (ROC) as the weight value and Multi Attribute Utility Theory (MAUT) as a solution. The alternatives used are Whatsapp, Messager, Discord, Telegram, Line, WeChatting and the criteria used are Security, Storage Media, Network Usage, application features, Display / Interface. The results obtained indicate that the Whatsapp alternative is the first recommendation as the best chatting application with a final value of 0.929456. The Telegram alternative became the second recommendation with a final value of 0.847485 and Line became the third recommendation with a final value of 0,624656.

QUALITY ASSESSMENT OF THE SOFTWARE DEVELOPMENT PROCESS OF AN IT COMPANY BASED ON THE USE OF THE UTILITY FUNCTION

The paper considers the software development process as an object of research, which is a poorly structured system. A description of such systems is given in the form of general characteristics, which include: difficulties in building an analytical model; incompleteness, inaccuracy, unreliability and uncertainty of information; benchmarks required for assessing weakly structured systems are often absent; uniqueness of the decision-making process; dynamic nature of models of poorly structured systems, etc. In this paper, the quality assessment of the software development process is considered based on maturity model standards, which can have continuous and discrete variants. The continuous version assesses the quality of the individual focus areas and processes of the maturity models. For this purpose, a discrete point scale of the first type is used, when the assessment is carried out according to an objective criterion. The quality assessment of individual focus areas and processes characterizes the local criteria for assessing the quality of the entire software development process. Therefore, the task is to form some kind of integral quality assessment on their basis. One of the options for solving this problem is a discrete maturity model, where the scale for assessing the entire software development process has five gradations called maturity levels. Starting from the second level, each gradation is characterized by a set of focus areas with corresponding levels of capability. The availability of such a scale allows not only assessing the quality of the entire software development process, but also solving the task of planning to improve its quality. But first, it is necessary to analyse such a scale from the point of view of its balance, namely, that the distances on the scale between the gradations are approximately equal. Therefore, the paper analyses the existing scales that can be proposed for expert assessment for the quality of the software development process. Their construction can be realized on the basis of a utility function using the local criteria of maturity models formalized in this paper. For this purpose, a fundamental property of systems is used. Namely, the dependence of the utility (efficiency) of a complex system on the invested resources over the life cycle interval, which usually takes the form of a logistic curve. Further research will be devoted to using this fact to build a balanced scale for assessing the entire software development process based on maturity models.

Robust Demand Estimation With Customer Choice-Based Models for Sales Transaction Data

We develop a novel statistical method to estimate customer choice among a firm’s portfolio of offerings when the firm cannot directly observe customers who choose not to purchase any product. This censored demand problem is prevalent in many industries such as hotels, airlines, and retail. Although several methods have been proposed to address this problem, they require some level of data aggregation across arrivals and/or choice sets, which results in information loss and potentially biased estimates. Therefore, they have limited applicability in an environment where the prices of a firm’s portfolio of offerings vary over time and sometimes even across different customers. Our proposed method combines several desirable properties, which makes it a better fit for realistic datasets where the available choice sets or attributes of the products in the choice sets change over time. We consider two additional types of information for identification of our model parameters: (1) additional mild assumptions on the customers’ utility function, and (2) external information about a firm’s market share. We then develop a robust estimation procedure that accounts for inaccuracies in either information type and let the data determine the best approach. Through Monte Carlo simulations, we show that our approach provides promising predictions of customer choice behavior when compared with other generally used methods and clearly outperforms those methods in scenarios where the product prices change frequently over time. Utilizing a real hotel transaction dataset provided by Oracle Labs, we further illustrate the improved estimation accuracy of our method compared to benchmark methods. Relative to existing approaches for estimating customer choice-based models, our proposed methodology better suits environments employing dynamic pricing and personalized offering practices, such as hospitality or online retailing.

ESG integration strategy with a multivariate normal distribution

Purpose: The paper aims to present a new framework for ESG integration strategies in portfolio optimization problems. The optimization in the new structure focuses on the portfolio level, and the procedure is not focused on utility functions or on preliminary weights applied to the asset level. It applies the resampling technique, and all the portfolios are optimal portfolios in the mean-variance space. It uses a filtering process where only optimal portfolios with lower ESG risks are considered. Therefore, this technique works only with optimized portfolios, avoids concentration bias, and considers estimation errors in the expected returns and in the covariance matrix. Design/methodology/approach: The sample mean returns and covariance matrices generated by a multivariate normal distribution are applied in mean-variance optimization to generate several portfolios in the efficient frontiers. An ESG filtering process is used to select portfolios with lower ESG risks from a sample of 42 companies listed on the Brazilian stock exchange with returns from the period of 2018/01/01 to 2024/04/22.Findings: Integration strategy costs may be lower than the best-in-class strategy costs and may be similar to the costs of a negative screening strategy. Social implications: The paper presents a framework that considers social, environmental, and governance factors in the portfolio optimization process.Originality: The main contribution of this paper is to present a new framework that combines resampling of returns’ mean and covariance based on a multivariate normal distribution with an ESG portfolio filtering process.

Assessing Voltage Stability in Distribution Networks: A Methodology Considering Correlation among Stochastic Variables

Distributed photovoltaic (PV) output exhibits strong stochasticity and weak adjustability. After being integrated with the network, its interaction with stochastic loads increases the difficulty of assessing the distribution network’s static voltage stability (SVS). In response to this issue, this article presents a probabilistic assessment method for SVS in a distribution network with distributed PV that considers the bilateral uncertainties and correlations on the source and load sides. The probabilistic models for the uncertain variables are established, with the correlation between stochastic variables described using the Copula function. The three-point estimate method (3PEM) based on the Nataf transformation is used to generate correlated samples. Continuous power flow (CPF) calculations are then performed on these samples to obtain the system’s critical voltage stability state. The distribution curves of critical voltage and load margin index (LMI) are fitted using Cornish-Fisher series. Finally, the utility function is introduced to establish the degree of risk of voltage instability under different scenarios, and the SVS assessment of the distribution network is completed. The IEEE 33-node distribution system is utilized to test the method presented, and the results across various scenarios highlight the method’s effectiveness.

A multicriteria decision model to improve emergency preparedness: Locating-allocating urban shelters against floods

The increasing prominence of natural disasters in recent years has made emergency disaster management one of the main challenges for building resilient societies. This way, the warming combination with human-induced urban interventions with climate change reshapes the strategic decision-making that public managers must deal with to promote safety and preparedness. Consequently, it implies complex situations under uncertainty that comprise conflicting dilemmas about which decision-makers (DMs) should establish trade-offs. Given this backdrop, this work proposes a risk-based multidimensional model for assessing urban shelter location against flood risk with Decision Analysis and Multi-Attribute Utility Theory. By using principles of the Queue Theory, the decision model undertakes four criteria that measure in a probabilistic manner the alternatives’ performance when considering urban flood disaster evacuation routes in affected areas. Altogether, it enables the DM to establish his/her preferences with views to rank potential locations for mounting temporary shelters and for planning emergency preparedness in risky areas. Being replicable to any urban context, our proposal has the potential to support and promote the development and implementation of crisis protocols consisting of measures to reduce flood impacts and adapt critical areas to climate change. From a numerical application of the proposed model, the DM is encouraged to explore graphical and tabular visualization tools, as well as sensitivity analysis, to establish in strategic terms how to design and implement emergency shelters to reduce flood risks in urban areas effectively.

Unpacking the discourse surrounding the impact agenda in the Hong Kong Research Assessment Exercise 2020

Abstract Research with economic utility and social value has been increasingly valued. Such an emphasis can be evidenced by the newly included assessment element of ‘societal impact’ in performance-based research funding (PBRF) schemes in different higher education systems around the world. This paper investigates how the non-academic impact is constructed and perceived in the Hong Kong Research Assessment Exercise (RAE) 2020, taking into account the local socio-cultural characteristics in the context of Hong Kong. Data sources include 13 impact case studies in the education panel submitted for the Hong Kong RAE 2020 and semi-structured interview with 17 education academics in Hong Kong. Findings revealed that the non-academic impact was constructed through a narrative pattern: (1) problem identification: tensions and synergies between local and international discourse; (2) problem resolution: prioritization of the evidence-based applied education research (with funding); (3) resolution dissemination: strategic employment of promotional genre. The paper discusses how decolonization, academic entrepreneurialism and collectivist culture have characterized the framing and understanding of the non-academic impact in the Hong Kong academia, contributing to the discourse on neoliberalism in higher education by providing a nuanced, local perspective on the impact agenda. Policy implications for a more localized and flexible impact agenda are also provided.

Economic utility of Colombian Romosinuano cattle

The objective of this study was to quantify the economic utility in Romosinuano production systems by developing a bioeconomic model assumed cow-calf, cow-calf plus stocker (CCPS), and complete cycle operations. Each system produced males for sale and females for replacement. Input parameters were established from breed data collected by AGROSAVIA. Revenues were estimated using the official cattle price, and production costs were quantified per activity. In the results, for cow-calf operations, the maximum economic utility was 244.12 USD. CCPS, yielded 231.86 USD, and Complete cycle, 268.94 USD. The genetic progress per generation for W240, W480, W24 and CI was + 3.8 kg, + 5 kg, + 5.9 kg, and -1 d, respectively. The price of livestock was the sensitized variable with the greatest impact on maximum economic utility (± 118.64 USD to ± 155.44 USD), followed by mineral supplementation (16.31 USD to ± 37.34 USD). The sensitized variables with the lowest impact were food (± 1.62 USD to ± 1.8 USD) and health plan supplies (± 6.03 USD to ± 9.13 USD). It is concluded that economic utility defined as a composite trait influenced by the characteristics that shape it favors genetic progress and the identification of animals with optimal performance in different bovine production systems.

Access strategies in mmWave cell-free network: A matching and auction theory based approach

The mmWave cell-free network is viewed as an energy-efficient and emerging architecture for beyond 5G systems. However, many practical issues should be solved for this system before the benefits are enjoyed, whereof feasible implementation of access strategy is one of the most vital. To this end, the access strategy including user scheduling and bandwidth allocation is investigated in this paper. Firstly, to maximize the defined utility function, the joint optimization problem of user scheduling and bandwidth allocation is formulated as a mixed integer nonlinear programming problem, which is intractable to search for an optimal solution in polynomial time. Secondly, a two-stage matching and sealed-auction-based access strategy is proposed to obtain a sub-optimal solution, where UEs with high-quality channel conditions are scheduled in the first-stage matching as much as possible, and the rest unmatched UEs are scheduled in the second-stage matching with the sealed-auction based bandwidth allocation. Thirdly, a simplified full-matching based access strategy with lower complexity is proposed for the high-dynamic scenario caused by the high-speed movement of UEs, and the complexities and convergence of the two proposed strategies are quantitatively analyzed. Finally, the performance of proposed strategies is evaluated through abundant simulations in different scenarios, and the superiorities of the proposed strategies are demonstrated through the comparison with reference strategies.

The Caring Life Course Theory: Opening new frontiers in care-A cardiac rehabilitation example.

To operationalize the Caring Life Course Theory (CLCT) as a framework for improving cardiac rehabilitation (CR) engagement and informing ways to address disparities in rural, low socio-economic areas. A secondary analysis of data collected from 15 CR programmes to identify CR patterns through the CLCT lens using a mixed-methods approach. All analytical processes were conducted in NVivo, coding qualitative data through thematic analysis based on CLCT constructs. Relationships among these constructs were quantitatively assessed using Jaccard coefficients and hierarchical clustering via dendrogram analysis to identify related clusters. A strong interconnectedness among constructs: 'care from others', 'capability', 'care network' and 'care provision' (coefficient = 1) highlights their entangled crucial role in CR. However, significant conceptual disparities between 'care biography' and 'fundamental care' (coefficient = 0.4) and between 'self-care' and 'care biography' (coefficient = 0.384615) indicate a need for more aligned and personalized care approaches within CR. The CLCT provides a comprehensive theoretical and practical framework to address disparities in CR, facilitating a personalized approach to enhance engagement in rural and underserved regions. Integrating CLCT into CR programme designs could effectively address participation challenges, demonstrating the theory's utility in developing targeted, accessible care interventions/solutions. Explored the challenge of low CR engagement in rural, low socio-economic settings. Uncovered care provision, transitions and individual care biographies' relevance for CR engagement. Demonstrated the potential of CLCT to inform/transform CR services for underserved populations, impacting practices and outcomes. EQUATOR-MMR-RHS. A consumer co-researcher contributed to all study phases.

Optimal portfolio and insurance strategy with biometric risks, habit formation and smooth ambiguity

This paper studies the optimal consumption, investment, health insurance and life insurance strategy for a wage earner with smooth ambiguity, habit formation and biometric risks. The individual can invest in the financial market composed of a risk-free asset and a risky asset whose unknown market price results in ambiguity. The habit formation depends on historical consumption and satisfies an ordinary differential equation. Moreover, the biometric risks, which consist of health shock risk and mortality risk, can impact the individual's income and health state. The individual can purchase health insurance and life insurance to respectively deal with health shock risk and mortality risk, and aims at maximizing the total expected utility of consumption, legacy and terminal wealth. Using the dynamic programming technique, we derive the corresponding Hamilton-Jacobi-Bellman equation in the states of health and critical illness respectively, prove the verification theorem and obtain closed-form solutions for the optimal strategies. Finally, numerical experiments are carried out to illustrate the impact of risk aversion, ambiguity aversion, health shock and habit formation on the optimal strategy. The results reveal that the wage earner with different utility functions and different health states will show different behaviors in consumption, investment and insurance purchase.

Disclosing Delivery Performance Information When Consumers Are Sensitive to Promised Delivery Time, Delivery Reliability, and Price

Problem definition: We investigate how the characteristics of consumers and a service firm influence the firm’s optimal pricing and promised delivery-time decisions as well as the optimal investment in the quality of delivery reliability information available to consumers. Methodology/results: We use utility, queuing, and choice modeling theories to model consumers’ behavior and to find solutions to the firm’s profit maximization problem. Managerial implications: The optimal strategy is to disclose either error-free delivery reliability information or no information at all. We also delineate conditions for each of the two strategies to dominate. Funding: This research was supported by the General Research Fund (GRF) of the Hong Kong Research Grants Council under Research Project LU13500822. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0223 .

The impact of national savings on economic development: a focused study on the ten poorest countries in Sub-Saharan Africa

The purpose of this study is to examine the impact of national savings on economic development, as measured by the Human Development Index (HDI), Inequality-adjusted HDI (iHDI), and Multidimensional Poverty Index (MPI), in ten of the poorest countries in Sub-Saharan Africa. The study employs a sequential Generalized Method of Moments (GMM) analysis to address potential endogeneity issues and account for the dynamic nature of the relationships, covering the period from 2009 to 2019. The findings reveal a complex relationship between national savings and the selected development indicators. While national savings exhibit positive impacts on HDI and iHDI, the results are not consistently statistically significant across all the sequential models. However, the analysis suggests that national savings have a positive influence on reducing multidimensional poverty, as measured by MPI, particularly when effectively channeled into productive investments. The study also highlights the significant positive impact of government expenditure and foreign direct investment (FDI) on human development, underscoring the importance of strategic public investments and foreign capital. The results suggest that while national savings are crucial, their effective utilization is essential for enhancing human development indices. Strategic investments in public goods and foreign capital are also important. The mixed effects of inflation and official development assistance (ODA) emphasize the need for stable economic policies and effective utilization of foreign aid. The modest positive impact of institutional quality suggests that improvements in governance and institutional frameworks can contribute to human development. The findings underscore the need for policies promoting financial inclusion, efficient public expenditure, foreign direct investment, and stable macroeconomic conditions to leverage national savings for economic development. The study’s findings provide valuable insights for policymakers in Sub-Saharan Africa, highlighting the need for comprehensive strategies that leverage national savings, public expenditure, and foreign investment to drive sustainable economic development and poverty reduction.

Dissolution behavior of aluminum and silicon from kaolinite diaspore symbiotic low-grade bauxite in low alkali system

China produces over 50 % of global alumina, but its dependence on imported bauxite is as high as 65 %. Local bauxite in China is of low grade. During mining, high-grade ores were sold, while low-grade ores were left piled up on site. This practice not only occupied a large land area but also caused serious pollution. Additionally, kaolinite and diaspore are embedded in a complex and dispersed distribution on the micron scale. The physical separation and ore phase dissociation of these two (kaolinite and diaspore) are very difficult, thereby preventing the economical and efficient utilization of Chinese bauxite using existing mineral flotation and sintering technologies. Based on the analysis of the molecular structure of kaolinite, this study found a significant difference in the dissolution rates of kaolinite and diaspore in a low-alkali solution. This conclusion was drawn through a combination of kinetic analysis and experimental exploration. Therefore, a new chemical pre desilication method of diaspore bauxite rich in kaolinite was proposed. The experiment found that at temperature of 180 ℃ and Na2O concentration of 20 g/L, the solubility of kaolinite in a low alkali solution was 3.3 g/L. After 40 minutes of reaction, the maximum dissolution of kaolinite reached 4.2 g/L. Under the same conditions, the solubility of diaspore was 0.064 g/L. The results of kinetic study showed that the activation energy of pure kaolinite dissolution reaction was 26.77 kJ/mol, which belongs to diffusion control. Conversely, the dissolution reaction activation energy of pure diaspore was 84.30 kJ/mol, which belongs to chemical reaction control. In low alkali systems, there was a significant difference in dissolution rate between the two. By optimizing the process conditions, the optimal process conditions were found. Under the optimal conditions, the aluminum silicon ratio increased from 3.22, 2.91, and 2.68–7.92, 8.42, and 8.17, respectively, by processing three types of low-grade bauxite with different particle sizes. It is noteworthy that this method can achieve a significant increase in bauxite A/S only requiring two simple processing steps: alkali dissolution and liquid-solid separation. This study not only fills the research and application gap of low alkali solutions in the field of bauxite beneficiation, but also helps to support the green and sustainable development of the world's alumina industry.

General equilibrium and dynamic inconsistency

We study the role of different sources of naivete in a general equilibrium version of the Ramsey model with quasi-hyperbolic discounting. When agents are aware of others’ naivete, as strongly suggested by empirical evidence, they revise consumption paths, correctly anticipating prices in a resulting sliding equilibrium (perfect foresight). When agents are unaware of others’ naivete, as is typically assumed in the theoretical literature, they revise both consumption paths and price expectations (quasi-perfect foresight). We prove the existence of sliding equilibrium under perfect foresight for the class of isoelastic utility functions. We show that generically quasi-hyperbolic discounting matters for saving behavior: sliding equilibrium under perfect foresight is observationally equivalent to some optimal path in the standard Ramsey model only if utility is logarithmic. By comparing sliding equilibria under different types of foresight we show that perfect foresight implies a higher saving rate, long-run capital stock and consumption level than quasi-perfect foresight.