{"title":"Rearranging Attributes in Networked Economies","authors":"Janelle Schlossberger","doi":"10.2139/ssrn.3845082","DOIUrl":"https://doi.org/10.2139/ssrn.3845082","url":null,"abstract":"This work studies an important departure from the classical networked economy. In the benchmark case, an external decision-making observer has full information about the networked economy. In this work, the observer does not have full information, yet must make decisions impacting the economy's agents. Specifically, the observer does not know how the attributes of the economy sit on the network's nodes. This work develops a complete, closed-form statistical approach that enables the observer to overcome this lack of information and still execute a decision. The observer must consider all possible arrangements of attributes on the network nodes. By exhaustively rearranging attributes, the observer can construct probability distributions that accurately characterize the economy. In this work, we first develop the necessary theoretical tools and we then show how the observer can employ these tools in the following settings: (1) education with peer effects, (2) consumption with network externalities, and (3) crime.","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125211042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Supplementary Materials for 'A ReMeDI for Microstructure Noise'","authors":"Z. Li, O. Linton","doi":"10.2139/ssrn.3788734","DOIUrl":"https://doi.org/10.2139/ssrn.3788734","url":null,"abstract":"This note contains the supplements to Li and Linton (2021).","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121178183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Determining the Number of Effective Parameters in Kernel Density Estimation","authors":"N. McCloud, Christopher F. Parmeter","doi":"10.2139/ssrn.3812831","DOIUrl":"https://doi.org/10.2139/ssrn.3812831","url":null,"abstract":"The hat matrix maps the vector of response values in a regression to its predicted counterpart. The trace of this hat matrix is the workhorse for calculating the effective number of parameters in both parametric and nonparametric regression settings. Drawing on the regression literature, the standard kernel density estimate is transformed to mimic a regression estimate thus allowing extraction of a usable hat matrix for calculating the effective number of parameters of the kernel density estimate. Asymptotic expressions for the trace of this hat matrix are derived under standard regularity conditions for mixed, continuous, and discrete densities. Simulations validate the theoretical contributions. Several empirical examples demonstrate the usefulness of the method suggesting that calculating the effective number of parameters of a kernel density estimator maybe useful in interpreting differences across estimators.","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133273104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conditional Hypothesis Testing","authors":"Kun Joo Michael Ang","doi":"10.2139/ssrn.3720414","DOIUrl":"https://doi.org/10.2139/ssrn.3720414","url":null,"abstract":"When testing multiple hypotheses, conventional techniques used for reducing false positives require all tests to be pre-specified and do not account for correlation between p-values. This makes them incompatible with sequential modelling techniques, where models are built one-at-a-time and future models benefit from the insight of previous testing. We propose here a technique for adjusting future tests to in-corporate prior information and show that this reduces to replacing the likelihood function with the conditional likelihood. A numerical algorithm is also developed that uses Monte Carlo integration to efficiently compute conditional acceptance regions from conditional sizes.","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127958287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Co-Evolution and Choice in Science and Technology Policy","authors":"I. Almudi, Francisco Fatas-Villafranca","doi":"10.2139/ssrn.3139715","DOIUrl":"https://doi.org/10.2139/ssrn.3139715","url":null,"abstract":"In this work, we develop the argument that technological progress emerges from the co-evolution between bodies of practice and bodies of understanding. We formally explore some consequences of the existence of mutually-dependent selection processes at work in both realms (co-evolution), and we extract implications regarding catalysts of co-evolution, as well as blocking factors. Then, we pick out blocking factors, and drawing on the concepts of fuzzy sets and choice structures, we propose a tentative strategy for decision-making within co-evolutionary environments. The analysis and the exploration of this proposal lead us to obtain a statistical index (the T–index) which may be relevant for science and technology policy. We suggest procedures for the application of the index, and we illustrate the whole analysis with a real case (the energy storage problem). Finally, we summarize our conclusions.","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126450764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Add or Multiply? A Tutorial on Ranking and Choosing with Multiple Criteria","authors":"C. Tofallis","doi":"10.2139/ssrn.3762021","DOIUrl":"https://doi.org/10.2139/ssrn.3762021","url":null,"abstract":"Simple additive weighting is a well-known method for scoring and ranking alternative options based on multiple attributes. However, the pitfalls associated with this approach are not widely appreciated. For example, the apparently innocuous step of normalizing the various attribute data in order to obtain comparable figures leads to markedly different rankings depending on which normalization is chosen. When the criteria are aggregated using multiplication, such difficulties are avoided because normalization is no longer required. This removes an important source of subjectivity in the analysis because the analyst no longer has to make a choice of normalization type. Moreover, it also permits the modelling of more realistic preference behaviour, such as diminishing marginal utility, which simple additive weighting does not provide. The multiplicative approach also has advantages when aggregating the ratings of panel members. This method is not new but has been ignored for too long by both practitioners and teachers. We aim to present it in a nontechnical way and illustrate its use with data on business schools.","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116980661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Consideration of Reputation Prediction of Ladygaga Using the Mathematical Model of Hit Phenomena","authors":"K. Yasuko, Genda Etsuo, Ishii Akira","doi":"10.5121/IJMA.2013.6107","DOIUrl":"https://doi.org/10.5121/IJMA.2013.6107","url":null,"abstract":"A mathematical model for the hit phenomenon in entertainment within a society is presented as a stochastic process of interactions of human dynamics. The calculations for the Japanese motion picture market based on to the mathematical model agree very well with the actual residue distribution in time. LADYGAGA are also analyzed using the data of SNS as well.","PeriodicalId":161214,"journal":{"name":"DecisionSciRN: Decision-Making in Mathematics (Topic)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116260996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}