Econometrics and decision making: Effects of presentation mode

Much of empirical economics involves regression analysis. However, does thepresentation of results affect economists ability to make inferences for decision makingpurposes? In a survey, 257 academic economists were asked to make probabilisticinferences on the basis of the outputs of a regression analysis presented in a standardformat. Questions concerned the distribution of the dependent variable conditional onknown values of the independent variable. However, many respondents underestimateduncertainty by failing to take into account the standard deviation of the estimatedresiduals. The addition of graphs did not substantially improve inferences. On the otherhand, when only graphs were provided (i.e., with no statistics), respondents weresubstantially more accurate. We discuss implications for improving practice in reportingresults of regression analyses.

The impact of gender composition on team performance and decision-making: Evidence from the field

We investigate whether the gender composition of teams affect theireconomic performance. We study a large business game, played in groups ofthree, where each group takes the role of a general manager. There are twoparallel competitions, one involving undergraduates and the other involvingMBAs. Our analysis shows that teams formed by three women aresignificantly outperformed by any other gender combination, both at theundergraduate and MBA levels. Looking across the performancedistribution, we find that for undergraduates, three women teams areoutperformed throughout, but by as much as 10pp at the bottom and by only1pp at the top. For MBAs, at the top, the best performing group is two menand one woman. The differences in performance are explained bydifferences in decision-making. We observe that three women teams are lessaggressive in their pricing strategies, invest less in R&D, and invest more insocial sustainability initiatives, than any other gender combination teams.Finally, we find support for the hypothesis that it is poor work dynamicsamong the three women teams that drives the results.

Decision making in uncertain and changing environments

We consider an agent who has to repeatedly make choices in an uncertainand changing environment, who has full information of the past, who discountsfuture payoffs, but who has no prior. We provide a learning algorithm thatperforms almost as well as the best of a given finite number of experts orbenchmark strategies and does so at any point in time, provided the agentis sufficiently patient. The key is to find the appropriate degree of forgettingdistant past. Standard learning algorithms that treat recent and distant pastequally do not have the sequential epsilon optimality property.

Surrogate decision making for patients with end-stage dementia.

OBJECTIVE: We aim to explore how health surrogates of patients with dementia proceed in decision making, which considerations are decisive, and whether family surrogates and professional guardians decide differently. METHODS: We conducted an experimental vignette study using think aloud protocol analysis. Thirty-two family surrogates and professional guardians were asked to decide on two hypothetical case vignettes, concerning a feeding tube placement and a cardiac pacemaker implantation in patients with end-stage dementia. They had to verbalize their thoughts while deciding. Verbalizations were audio-recorded, transcribed, and analyzed according to content analysis. By experimentally changing variables in the vignettes, the impact of these variables on the outcome of decision making was calculated. RESULTS: Although only 25% and 31% of the relatives gave their consent to the feeding tube and pacemaker placement, respectively, 56% and 81% of the professional guardians consented to these life-sustaining measures. Relatives decided intuitively, referred to their own preferences, and focused on the patient's age, state of wellbeing, and suffering. Professional guardians showed a deliberative approach, relied on medical and legal authorities, and emphasized patient autonomy. Situational variables such as the patient's current behavior and the views of health care professionals and family members had higher impacts on decisions than the patient's prior statements or life attitudes. CONCLUSIONS: Both the process and outcome of surrogate decision making depend heavily on whether the surrogate is a relative or not. These findings have implications for the physician-surrogate relationship and legal frameworks regarding surrogacy. Copyright © 2011 John Wiley & Sons, Ltd.

Induced aggregation operators in decision making with the Dempster-Shafer belief structure

We study the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging (IOWA) operator and the induced ordered weighted geometric (IOWG) operator. We then analyze the problem of decision making with Dempster-Shafer theory of evidence. We suggest the use of induced aggregation operators in decision making with Dempster-Shafer theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained using different types of aggregation operators can be seen.

The induced 2-tuple linguistic generalized OWA operator and its application in linguistic decision making

[spa] Se presenta el operador de media ponderada ordenada generalizada lingüística de 2 tuplas inducida (2-TILGOWA). Es un nuevo operador de agregación que extiende los anteriores modelos a través de utilizar medias generalizadas, variables de ordenación inducidas e información lingüística representada mediante el modelo de las 2 tuplas lingüísticas. Su principal ventaja se encuentra en la posibilidad de incluir a un gran número de operadores de agregación lingüísticos como casos particulares. Por eso, el análisis puede ser visto desde diferentes perspectivas de forma que se obtiene una visión más completa del problema considerado y seleccionar la alternativa que parece estar en mayor concordancia con nuestros intereses o creencias. A continuación se desarrolla una generalización mayor a través de utilizar medias cuasi-aritméticas, obteniéndose el operador Quasi-2-TILOWA. El trabajo finaliza analizando la aplicabilidad del nuevo modelo en un problema de toma de decisiones sobre gestión de la producción.

The generalized index of maximum and minimum level and its application in decision making

[spa] El índice del máximo y el mínimo nivel es una técnica muy útil, especialmente para toma de decisiones, que usa la distancia de Hamming y el coeficiente de adecuación en el mismo problema. En este trabajo, se propone una generalización a través de utilizar medias generalizadas y cuasi aritméticas. A estos operadores de agregación, se les denominará el índice del máximo y el mínimo nivel medio ponderado ordenado generalizado (GOWAIMAM) y cuasi aritmético (Quasi-OWAIMAM). Estos nuevos operadores generalizan una amplia gama de casos particulares como el índice del máximo y el mínimo nivel generalizado (GIMAM), el OWAIMAM, y otros. También se desarrolla una aplicación en la toma de decisiones sobre selección de productos.

Induced aggregation operators in decision making with the Dempster-Shafer belief structure

We study the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging (IOWA) operator and the induced ordered weighted geometric (IOWG) operator. We then analyze the problem of decision making with Dempster-Shafer theory of evidence. We suggest the use of induced aggregation operators in decision making with Dempster-Shafer theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained using different types of aggregation operators can be seen.

The induced 2-tuple linguistic generalized OWA operator and its application in linguistic decision making

[spa] Se presenta el operador de media ponderada ordenada generalizada lingüística de 2 tuplas inducida (2-TILGOWA). Es un nuevo operador de agregación que extiende los anteriores modelos a través de utilizar medias generalizadas, variables de ordenación inducidas e información lingüística representada mediante el modelo de las 2 tuplas lingüísticas. Su principal ventaja se encuentra en la posibilidad de incluir a un gran número de operadores de agregación lingüísticos como casos particulares. Por eso, el análisis puede ser visto desde diferentes perspectivas de forma que se obtiene una visión más completa del problema considerado y seleccionar la alternativa que parece estar en mayor concordancia con nuestros intereses o creencias. A continuación se desarrolla una generalización mayor a través de utilizar medias cuasi-aritméticas, obteniéndose el operador Quasi-2-TILOWA. El trabajo finaliza analizando la aplicabilidad del nuevo modelo en un problema de toma de decisiones sobre gestión de la producción.

The generalized index of maximum and minimum level and its application in decision making

[spa] El índice del máximo y el mínimo nivel es una técnica muy útil, especialmente para toma de decisiones, que usa la distancia de Hamming y el coeficiente de adecuación en el mismo problema. En este trabajo, se propone una generalización a través de utilizar medias generalizadas y cuasi aritméticas. A estos operadores de agregación, se les denominará el índice del máximo y el mínimo nivel medio ponderado ordenado generalizado (GOWAIMAM) y cuasi aritmético (Quasi-OWAIMAM). Estos nuevos operadores generalizan una amplia gama de casos particulares como el índice del máximo y el mínimo nivel generalizado (GIMAM), el OWAIMAM, y otros. También se desarrolla una aplicación en la toma de decisiones sobre selección de productos.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary