The Experts below are selected from a list of 813 Experts worldwide ranked by ideXlab platform
Joan Roughgarden - One of the best experts on this subject based on the ideXlab platform.
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The Perfect Family: Decision Making in Biparental Care
PloS one, 2009Co-Authors: Erol Akçay, Joan RoughgardenAbstract:Background: Previous theoretical work on parental decisions in biparental care has emphasized the role of the conflict between evolutionary interests of parents in these decisions. A prominent prediction from this work is that parents should compensate for decreases in each other’s effort, but only partially so. However, experimental tests that manipulate parents and measure their responses fail to confirm this prediction. At the same time, the process of parental decision making has remained unexplored theoretically. We develop a model to address the discrepancy between experiments and the theoretical prediction, and explore how assuming different decision making processes changes the prediction from the theory. Model Description: We assume that parents make decisions in behavioral time. They have a fixed time budget, and allocate it between two parental tasks: provisioning the offspring and defending the nest. The Proximate Determinant of the allocation decisions are parents’ behavioral objectives. We assume both parents aim to maximize the offspring production from the nest. Experimental manipulations change the shape of the nest production function. We consider two different scenarios for how parents make decisions: one where parents communicate with each other and act together (the perfect family), and one where they do not communicate, and act independently (the almost perfect family). Conclusions/Significance: The perfect family model is able to generate all the types of responses seen in experimental studies. The kind of response predicted depends on the nest production function, i.e. how parents’ allocations affect offspring production, and the type of experimental manipulation. In particular, we find that complementarity of parents’ allocations promotes matching responses. In contrast, the relative responses do not depend on the type of manipulation in the almost perfect family model. These results highlight the importance of the interaction between nest production function and how parents make decisions, factors that have largely been overlooked in previous models.
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The perfect family: decision making in biparental care. PloS One 4:e7345
2009Co-Authors: Joan RoughgardenAbstract:Background: Previous theoretical work on parental decisions in biparental care has emphasized the role of the conflict between evolutionary interests of parents in these decisions. A prominent prediction from this work is that parents should compensate for decreases in each other’s effort, but only partially so. However, experimental tests that manipulate parents and measure their responses fail to confirm this prediction. At the same time, the process of parental decision making has remained unexplored theoretically. We develop a model to address the discrepancy between experiments and the theoretical prediction, and explore how assuming different decision making processes changes the prediction from the theory. Model Description: We assume that parents make decisions in behavioral time. They have a fixed time budget, and allocate it between two parental tasks: provisioning the offspring and defending the nest. The Proximate Determinant of the allocation decisions are parents ’ behavioral objectives. We assume both parents aim to maximize the offspring production from the nest. Experimental manipulations change the shape of the nest production function. We consider two different scenarios for how parents make decisions: one where parents communicate with each other and act together (the perfect family), and one where they do not communicate, and act independently (the almost perfect family). Conclusions/Significance: The perfect family model is able to generate all the types of responses seen in experimenta
Erol Akçay - One of the best experts on this subject based on the ideXlab platform.
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The Perfect Family: Decision Making in Biparental Care
PloS one, 2009Co-Authors: Erol Akçay, Joan RoughgardenAbstract:Background: Previous theoretical work on parental decisions in biparental care has emphasized the role of the conflict between evolutionary interests of parents in these decisions. A prominent prediction from this work is that parents should compensate for decreases in each other’s effort, but only partially so. However, experimental tests that manipulate parents and measure their responses fail to confirm this prediction. At the same time, the process of parental decision making has remained unexplored theoretically. We develop a model to address the discrepancy between experiments and the theoretical prediction, and explore how assuming different decision making processes changes the prediction from the theory. Model Description: We assume that parents make decisions in behavioral time. They have a fixed time budget, and allocate it between two parental tasks: provisioning the offspring and defending the nest. The Proximate Determinant of the allocation decisions are parents’ behavioral objectives. We assume both parents aim to maximize the offspring production from the nest. Experimental manipulations change the shape of the nest production function. We consider two different scenarios for how parents make decisions: one where parents communicate with each other and act together (the perfect family), and one where they do not communicate, and act independently (the almost perfect family). Conclusions/Significance: The perfect family model is able to generate all the types of responses seen in experimental studies. The kind of response predicted depends on the nest production function, i.e. how parents’ allocations affect offspring production, and the type of experimental manipulation. In particular, we find that complementarity of parents’ allocations promotes matching responses. In contrast, the relative responses do not depend on the type of manipulation in the almost perfect family model. These results highlight the importance of the interaction between nest production function and how parents make decisions, factors that have largely been overlooked in previous models.
Richard K Lyons - One of the best experts on this subject based on the ideXlab platform.
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Private correspondence
2007Co-Authors: Martin D D Evans, Richard K LyonsAbstract:Macroeconomic models of nominal exchange rates perform poorly. In sample, R 2 statistics as high as 10 percent are rare. Out of sample, these models are typically out-forecast by a naïve random walk. This paper presents a model of a new kind. Instead of relying exclusively on macroeconomic Determinants, the model includes a Determinant from the field of microstructure—order flow. Order flow is the Proximate Determinant of price in all microstructure models. This is a radically different approach to exchange rate determination. It is also strikingly successful in accounting for realized rates. Our model of daily exchange-rate changes produces R 2 statistics above 50 percent. Out of sample, our model produces significantly better short-horizon forecasts than a random walk. For the DM/ $ spot market as a whole, we find that $1 billion of net dollar purchases increases the DM price of a dollar by about 1 pfennig
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order flow and exchange rate dynamics
National Bureau of Economic Research, 1999Co-Authors: Martin D D Evans, Richard K LyonsAbstract:Macroeconomic models of nominal exchange rates perform poorly. In sample, R2 statistics as high as 10 percent are rare. Out of sample, these models are typically out-forecast by a na‹ve random walk. This paper presents a model of a new kind. Instead of relying exclusively on macroeconomic Determinants, the model includes a Determinant from the field of microstructure-order flow. Order flow is the Proximate Determinant of price in all microstructure models. This is a radically different approach to exchange rate determination. It is also strikingly successful in accounting for realized rates. Our model of daily exchange-rate changes produces R2 statistics above 50 percent. Out of sample, our model produces significantly better short-horizon forecasts than a random walk. For the DM/$ spot market as a whole, we find that $1 billion of net dollar purchases increases the DM price of a dollar by about 1 pfennig.
Ross, David A - One of the best experts on this subject based on the ideXlab platform.
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HIV Infection among Young People in Northwest Tanzania: The Role of Biological, Behavioural and Socio-Demographic Risk Factors.
'Public Library of Science (PLoS)', 2013Co-Authors: Lemme Francesca, Doyle, Aoife M, Changalucha John, Andreasen Aura, Baisley Kathy, Maganja Kaballa, Watson-jones Deborah, Kapiga Saidi, Hayes, Richard J, Ross, David AAbstract:BACKGROUND: Young people are at high risk of HIV and developing appropriate prevention programmes requires an understanding of the risk factors for HIV in this age group. We investigated factors associated with HIV among participants aged 15-30 years in a 2007-8 cross-sectional survey nested within a community-randomised trial of the MEMA kwa Vijana intervention in 20 rural communities in northwest Tanzania. METHODS: We analysed data for 7259(53%) males and 6476(47%) females. Using a Proximate-Determinant conceptual framework and conditional logistic regression, we obtained sex-specific Odds Ratios (ORs) for the association of HIV infection with socio-demographic, knowledge, behavioural and biological factors. RESULTS: HSV-2 infection was strongly associated with HIV infection (females: adjOR 4.4, 95%CI 3.2-6.1; males: adjOR 4.2, 95%CI 2.8-6.2). Several socio-demographic factors (such as age, marital status and mobility), behavioural factors (condom use, number and type of sexual partnerships) and biological factors (blood transfusion, lifetime pregnancies, genital ulcers, Neisseria gonorrhoeae) were also associated with HIV infection. Among females, lifetime sexual partners (linear trend, p
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HIV Infection among Young People in Northwest Tanzania: The Role of Biological, Behavioural and Socio-Demographic Risk Factors.
Public Library of Science, 2013Co-Authors: Lemme Francesca, Doyle, Aoife M, Changalucha John, Andreasen Aura, Baisley Kathy, Maganja Kaballa, Watson-jones Deborah, Kapiga Saidi, Hayes, Richard J, Ross, David AAbstract:Young people are at high risk of HIV and developing appropriate prevention programmes requires an understanding of the risk factors for HIV in this age group. We investigated factors associated with HIV among participants aged 15-30 years in a 2007-8 cross-sectional survey nested within a community-randomised trial of the MEMA kwa Vijana intervention in 20 rural communities in northwest Tanzania. We analysed data for 7259(53%) males and 6476(47%) females. Using a Proximate-Determinant conceptual framework and conditional logistic regression, we obtained sex-specific Odds Ratios (ORs) for the association of HIV infection with socio-demographic, knowledge, behavioural and biological factors. HSV-2 infection was strongly associated with HIV infection (females: adjOR 4.4, 95%CI 3.2-6.1; males: adjOR 4.2, 95%CI 2.8-6.2). Several socio-demographic factors (such as age, marital status and mobility), behavioural factors (condom use, number and type of sexual partnerships) and biological factors (blood transfusion, lifetime pregnancies, genital ulcers, Neisseria gonorrhoeae) were also associated with HIV infection. Among females, lifetime sexual partners (linear trend, p
Lemme Francesca - One of the best experts on this subject based on the ideXlab platform.
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HIV Infection among Young People in Northwest Tanzania: The Role of Biological, Behavioural and Socio-Demographic Risk Factors.
'Public Library of Science (PLoS)', 2013Co-Authors: Lemme Francesca, Doyle, Aoife M, Changalucha John, Andreasen Aura, Baisley Kathy, Maganja Kaballa, Watson-jones Deborah, Kapiga Saidi, Hayes, Richard J, Ross, David AAbstract:BACKGROUND: Young people are at high risk of HIV and developing appropriate prevention programmes requires an understanding of the risk factors for HIV in this age group. We investigated factors associated with HIV among participants aged 15-30 years in a 2007-8 cross-sectional survey nested within a community-randomised trial of the MEMA kwa Vijana intervention in 20 rural communities in northwest Tanzania. METHODS: We analysed data for 7259(53%) males and 6476(47%) females. Using a Proximate-Determinant conceptual framework and conditional logistic regression, we obtained sex-specific Odds Ratios (ORs) for the association of HIV infection with socio-demographic, knowledge, behavioural and biological factors. RESULTS: HSV-2 infection was strongly associated with HIV infection (females: adjOR 4.4, 95%CI 3.2-6.1; males: adjOR 4.2, 95%CI 2.8-6.2). Several socio-demographic factors (such as age, marital status and mobility), behavioural factors (condom use, number and type of sexual partnerships) and biological factors (blood transfusion, lifetime pregnancies, genital ulcers, Neisseria gonorrhoeae) were also associated with HIV infection. Among females, lifetime sexual partners (linear trend, p
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HIV Infection among Young People in Northwest Tanzania: The Role of Biological, Behavioural and Socio-Demographic Risk Factors.
Public Library of Science, 2013Co-Authors: Lemme Francesca, Doyle, Aoife M, Changalucha John, Andreasen Aura, Baisley Kathy, Maganja Kaballa, Watson-jones Deborah, Kapiga Saidi, Hayes, Richard J, Ross, David AAbstract:Young people are at high risk of HIV and developing appropriate prevention programmes requires an understanding of the risk factors for HIV in this age group. We investigated factors associated with HIV among participants aged 15-30 years in a 2007-8 cross-sectional survey nested within a community-randomised trial of the MEMA kwa Vijana intervention in 20 rural communities in northwest Tanzania. We analysed data for 7259(53%) males and 6476(47%) females. Using a Proximate-Determinant conceptual framework and conditional logistic regression, we obtained sex-specific Odds Ratios (ORs) for the association of HIV infection with socio-demographic, knowledge, behavioural and biological factors. HSV-2 infection was strongly associated with HIV infection (females: adjOR 4.4, 95%CI 3.2-6.1; males: adjOR 4.2, 95%CI 2.8-6.2). Several socio-demographic factors (such as age, marital status and mobility), behavioural factors (condom use, number and type of sexual partnerships) and biological factors (blood transfusion, lifetime pregnancies, genital ulcers, Neisseria gonorrhoeae) were also associated with HIV infection. Among females, lifetime sexual partners (linear trend, p
