The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.

In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in the ensuing two centuries due to technology-driven productivity growth.

Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.

The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.

The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.

This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.

While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.

The primary purpose of the passage is to

The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.

In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in the ensuing two centuries due to technology-driven productivity growth.

Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.

The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.

The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.

This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.

While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.

The primary purpose of the passage is to

The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.

In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in the ensuing two centuries due to technology-driven productivity growth.

Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.

The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.

The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.

This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.

While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.

The primary purpose of the passage is to

The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.

In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in the ensuing two centuries due to technology-driven productivity growth.

Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.

The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.

The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.

This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.

While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.

The primary purpose of the passage is to

The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.

In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in the ensuing two centuries due to technology-driven productivity growth.

Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.

The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.

The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.

This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.

While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.

The primary purpose of the passage is to

This passage is adapted from Adam K. Fetterman and Kai Sassenberg, “The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists", first published in December 2015 by PLOS ONE.

We like to think of science as a purely rational. However, scientists are human and often identify with their work. Therefore, it should not be controversial to suggest that emotions are involved in replication discussions. Adding to this inherently emotionally volatile situation, the recent increase in the use of social media and blogs by scientists has allowed for instantaneous, unfiltered, and at times emotion-based commentary on research. Certainly social media has the potential to lead to many positive outcomes in science–among others, to create a more open science. To some, however, it seems as if this ease of communication is also leading to the public tar and feathering of scientists. Whether these assertions are true is up for debate, but we assume they are a part of many scientists’ subjective reality. Indeed, when failed replications are discussed in the same paragraphs as questionable research practices, or even fraud, it is hard to separate the science from the scientist. Questionable research practices and fraud are not about the science; they are about the scientist. We believe that these considerations are at least part of the reason that we find the overestimation effect that we do, here.

Even so, the current data suggests that while many are worried about how a failed replication would affect their reputation, it is probably not as bad as they think. Of course, the current data cannot provide evidence that there are no negative effects; just that the negative impact is overestimated. That said, everyone wants to be seen as competent and honest, but failed replications are a part of science. In fact, they are how science moves forward!

While we imply that these effects may be exacerbated by social media, the data cannot directly speak to this. However, any one of a number of cognitive biases may add support to this assumption and explain our findings. For example, it may be that a type of availability bias or pluralistic ignorance of which the more vocal and critical voices are leading individuals to judge current opinions as more negative than reality. As a result, it is easy to conflate discussions about direct replications with “witch- hunts” and overestimate the impact on one’s own reputation. Whatever the source may be, it is worth looking at the potential negative impact of social media in scientific conversations.

If the desire is to move science forward, scientists need to be able to acknowledge when they are wrong. Theories come and go, and scientists learn from their mistakes (if they can even be called “mistakes”). This is the point of science. However, holding on to faulty ideas flies in the face of the scientific method. Even so, it often seems as if scientists have a hard time admitting wrongness. This seems doubly true when someone else fails to replicate a scientist’s findings. In some cases, this may be the proper response. Just as often, though, it is not. In most cases, admitting wrongness will have relatively fewer ill effects on one’s reputation than not admitting and it may be better for reputation. It could also be that wrongness admission repairs damage to reputation.

It may seem strange that others consider it less likely that questionable research practices, for example, were used when a scientist admits that they were wrong. However, it does make sense from the standpoint that wrongness admission seems to indicate honesty. Therefore, if one is honest in one domain, they are likely honest in other domains. Moreover, the refusal to admit might indicate to others that the original scientist is trying to cover something up. The lack of significance of most of the interactions in our study suggests that it even seems as if scientists might already realize this. Therefore, we can generally suggest that scientists admit they are wrong, but only when the evidence suggests they should.

The chart below maps how scientists view others' work (left) and how they suspect others will view their own work (right) if the researcher (the scientist or another, depending on the focus) admitted to engaging in questionable research practices.

Adapted from Fetterman & Sassenberg, "The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists." December 9, 2015, PLOS One.

The primary purpose of the passage is to

This passage is adapted from Adam K. Fetterman and Kai Sassenberg, “The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists", first published in December 2015 by PLOS ONE.

We like to think of science as a purely rational. However, scientists are human and often identify with their work. Therefore, it should not be controversial to suggest that emotions are involved in replication discussions. Adding to this inherently emotionally volatile situation, the recent increase in the use of social media and blogs by scientists has allowed for instantaneous, unfiltered, and at times emotion-based commentary on research. Certainly social media has the potential to lead to many positive outcomes in science–among others, to create a more open science. To some, however, it seems as if this ease of communication is also leading to the public tar and feathering of scientists. Whether these assertions are true is up for debate, but we assume they are a part of many scientists’ subjective reality. Indeed, when failed replications are discussed in the same paragraphs as questionable research practices, or even fraud, it is hard to separate the science from the scientist. Questionable research practices and fraud are not about the science; they are about the scientist. We believe that these considerations are at least part of the reason that we find the overestimation effect that we do, here.

Even so, the current data suggests that while many are worried about how a failed replication would affect their reputation, it is probably not as bad as they think. Of course, the current data cannot provide evidence that there are no negative effects; just that the negative impact is overestimated. That said, everyone wants to be seen as competent and honest, but failed replications are a part of science. In fact, they are how science moves forward!

While we imply that these effects may be exacerbated by social media, the data cannot directly speak to this. However, any one of a number of cognitive biases may add support to this assumption and explain our findings. For example, it may be that a type of availability bias or pluralistic ignorance of which the more vocal and critical voices are leading individuals to judge current opinions as more negative than reality. As a result, it is easy to conflate discussions about direct replications with “witch- hunts” and overestimate the impact on one’s own reputation. Whatever the source may be, it is worth looking at the potential negative impact of social media in scientific conversations.

If the desire is to move science forward, scientists need to be able to acknowledge when they are wrong. Theories come and go, and scientists learn from their mistakes (if they can even be called “mistakes”). This is the point of science. However, holding on to faulty ideas flies in the face of the scientific method. Even so, it often seems as if scientists have a hard time admitting wrongness. This seems doubly true when someone else fails to replicate a scientist’s findings. In some cases, this may be the proper response. Just as often, though, it is not. In most cases, admitting wrongness will have relatively fewer ill effects on one’s reputation than not admitting and it may be better for reputation. It could also be that wrongness admission repairs damage to reputation.

It may seem strange that others consider it less likely that questionable research practices, for example, were used when a scientist admits that they were wrong. However, it does make sense from the standpoint that wrongness admission seems to indicate honesty. Therefore, if one is honest in one domain, they are likely honest in other domains. Moreover, the refusal to admit might indicate to others that the original scientist is trying to cover something up. The lack of significance of most of the interactions in our study suggests that it even seems as if scientists might already realize this. Therefore, we can generally suggest that scientists admit they are wrong, but only when the evidence suggests they should.

The chart below maps how scientists view others' work (left) and how they suspect others will view their own work (right) if the researcher (the scientist or another, depending on the focus) admitted to engaging in questionable research practices.

Adapted from Fetterman & Sassenberg, "The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists." December 9, 2015, PLOS One.

The primary purpose of the passage is to

This passage is adapted from Adam K. Fetterman and Kai Sassenberg, “The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists", first published in December 2015 by PLOS ONE.

We like to think of science as a purely rational. However, scientists are human and often identify with their work. Therefore, it should not be controversial to suggest that emotions are involved in replication discussions. Adding to this inherently emotionally volatile situation, the recent increase in the use of social media and blogs by scientists has allowed for instantaneous, unfiltered, and at times emotion-based commentary on research. Certainly social media has the potential to lead to many positive outcomes in science–among others, to create a more open science. To some, however, it seems as if this ease of communication is also leading to the public tar and feathering of scientists. Whether these assertions are true is up for debate, but we assume they are a part of many scientists’ subjective reality. Indeed, when failed replications are discussed in the same paragraphs as questionable research practices, or even fraud, it is hard to separate the science from the scientist. Questionable research practices and fraud are not about the science; they are about the scientist. We believe that these considerations are at least part of the reason that we find the overestimation effect that we do, here.

Even so, the current data suggests that while many are worried about how a failed replication would affect their reputation, it is probably not as bad as they think. Of course, the current data cannot provide evidence that there are no negative effects; just that the negative impact is overestimated. That said, everyone wants to be seen as competent and honest, but failed replications are a part of science. In fact, they are how science moves forward!

While we imply that these effects may be exacerbated by social media, the data cannot directly speak to this. However, any one of a number of cognitive biases may add support to this assumption and explain our findings. For example, it may be that a type of availability bias or pluralistic ignorance of which the more vocal and critical voices are leading individuals to judge current opinions as more negative than reality. As a result, it is easy to conflate discussions about direct replications with “witch- hunts” and overestimate the impact on one’s own reputation. Whatever the source may be, it is worth looking at the potential negative impact of social media in scientific conversations.

If the desire is to move science forward, scientists need to be able to acknowledge when they are wrong. Theories come and go, and scientists learn from their mistakes (if they can even be called “mistakes”). This is the point of science. However, holding on to faulty ideas flies in the face of the scientific method. Even so, it often seems as if scientists have a hard time admitting wrongness. This seems doubly true when someone else fails to replicate a scientist’s findings. In some cases, this may be the proper response. Just as often, though, it is not. In most cases, admitting wrongness will have relatively fewer ill effects on one’s reputation than not admitting and it may be better for reputation. It could also be that wrongness admission repairs damage to reputation.

It may seem strange that others consider it less likely that questionable research practices, for example, were used when a scientist admits that they were wrong. However, it does make sense from the standpoint that wrongness admission seems to indicate honesty. Therefore, if one is honest in one domain, they are likely honest in other domains. Moreover, the refusal to admit might indicate to others that the original scientist is trying to cover something up. The lack of significance of most of the interactions in our study suggests that it even seems as if scientists might already realize this. Therefore, we can generally suggest that scientists admit they are wrong, but only when the evidence suggests they should.

The chart below maps how scientists view others' work (left) and how they suspect others will view their own work (right) if the researcher (the scientist or another, depending on the focus) admitted to engaging in questionable research practices.

Adapted from Fetterman & Sassenberg, "The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists." December 9, 2015, PLOS One.

The primary purpose of the passage is to

This passage is adapted from Adam K. Fetterman and Kai Sassenberg, “The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists", first published in December 2015 by PLOS ONE.

We like to think of science as a purely rational. However, scientists are human and often identify with their work. Therefore, it should not be controversial to suggest that emotions are involved in replication discussions. Adding to this inherently emotionally volatile situation, the recent increase in the use of social media and blogs by scientists has allowed for instantaneous, unfiltered, and at times emotion-based commentary on research. Certainly social media has the potential to lead to many positive outcomes in science–among others, to create a more open science. To some, however, it seems as if this ease of communication is also leading to the public tar and feathering of scientists. Whether these assertions are true is up for debate, but we assume they are a part of many scientists’ subjective reality. Indeed, when failed replications are discussed in the same paragraphs as questionable research practices, or even fraud, it is hard to separate the science from the scientist. Questionable research practices and fraud are not about the science; they are about the scientist. We believe that these considerations are at least part of the reason that we find the overestimation effect that we do, here.

Even so, the current data suggests that while many are worried about how a failed replication would affect their reputation, it is probably not as bad as they think. Of course, the current data cannot provide evidence that there are no negative effects; just that the negative impact is overestimated. That said, everyone wants to be seen as competent and honest, but failed replications are a part of science. In fact, they are how science moves forward!

While we imply that these effects may be exacerbated by social media, the data cannot directly speak to this. However, any one of a number of cognitive biases may add support to this assumption and explain our findings. For example, it may be that a type of availability bias or pluralistic ignorance of which the more vocal and critical voices are leading individuals to judge current opinions as more negative than reality. As a result, it is easy to conflate discussions about direct replications with “witch- hunts” and overestimate the impact on one’s own reputation. Whatever the source may be, it is worth looking at the potential negative impact of social media in scientific conversations.

If the desire is to move science forward, scientists need to be able to acknowledge when they are wrong. Theories come and go, and scientists learn from their mistakes (if they can even be called “mistakes”). This is the point of science. However, holding on to faulty ideas flies in the face of the scientific method. Even so, it often seems as if scientists have a hard time admitting wrongness. This seems doubly true when someone else fails to replicate a scientist’s findings. In some cases, this may be the proper response. Just as often, though, it is not. In most cases, admitting wrongness will have relatively fewer ill effects on one’s reputation than not admitting and it may be better for reputation. It could also be that wrongness admission repairs damage to reputation.

It may seem strange that others consider it less likely that questionable research practices, for example, were used when a scientist admits that they were wrong. However, it does make sense from the standpoint that wrongness admission seems to indicate honesty. Therefore, if one is honest in one domain, they are likely honest in other domains. Moreover, the refusal to admit might indicate to others that the original scientist is trying to cover something up. The lack of significance of most of the interactions in our study suggests that it even seems as if scientists might already realize this. Therefore, we can generally suggest that scientists admit they are wrong, but only when the evidence suggests they should.

The chart below maps how scientists view others' work (left) and how they suspect others will view their own work (right) if the researcher (the scientist or another, depending on the focus) admitted to engaging in questionable research practices.

Adapted from Fetterman & Sassenberg, "The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists." December 9, 2015, PLOS One.

The primary purpose of the passage is to

This passage is adapted from Adam K. Fetterman and Kai Sassenberg, “The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists", first published in December 2015 by PLOS ONE.

We like to think of science as a purely rational. However, scientists are human and often identify with their work. Therefore, it should not be controversial to suggest that emotions are involved in replication discussions. Adding to this inherently emotionally volatile situation, the recent increase in the use of social media and blogs by scientists has allowed for instantaneous, unfiltered, and at times emotion-based commentary on research. Certainly social media has the potential to lead to many positive outcomes in science–among others, to create a more open science. To some, however, it seems as if this ease of communication is also leading to the public tar and feathering of scientists. Whether these assertions are true is up for debate, but we assume they are a part of many scientists’ subjective reality. Indeed, when failed replications are discussed in the same paragraphs as questionable research practices, or even fraud, it is hard to separate the science from the scientist. Questionable research practices and fraud are not about the science; they are about the scientist. We believe that these considerations are at least part of the reason that we find the overestimation effect that we do, here.

Even so, the current data suggests that while many are worried about how a failed replication would affect their reputation, it is probably not as bad as they think. Of course, the current data cannot provide evidence that there are no negative effects; just that the negative impact is overestimated. That said, everyone wants to be seen as competent and honest, but failed replications are a part of science. In fact, they are how science moves forward!

While we imply that these effects may be exacerbated by social media, the data cannot directly speak to this. However, any one of a number of cognitive biases may add support to this assumption and explain our findings. For example, it may be that a type of availability bias or pluralistic ignorance of which the more vocal and critical voices are leading individuals to judge current opinions as more negative than reality. As a result, it is easy to conflate discussions about direct replications with “witch- hunts” and overestimate the impact on one’s own reputation. Whatever the source may be, it is worth looking at the potential negative impact of social media in scientific conversations.

If the desire is to move science forward, scientists need to be able to acknowledge when they are wrong. Theories come and go, and scientists learn from their mistakes (if they can even be called “mistakes”). This is the point of science. However, holding on to faulty ideas flies in the face of the scientific method. Even so, it often seems as if scientists have a hard time admitting wrongness. This seems doubly true when someone else fails to replicate a scientist’s findings. In some cases, this may be the proper response. Just as often, though, it is not. In most cases, admitting wrongness will have relatively fewer ill effects on one’s reputation than not admitting and it may be better for reputation. It could also be that wrongness admission repairs damage to reputation.

It may seem strange that others consider it less likely that questionable research practices, for example, were used when a scientist admits that they were wrong. However, it does make sense from the standpoint that wrongness admission seems to indicate honesty. Therefore, if one is honest in one domain, they are likely honest in other domains. Moreover, the refusal to admit might indicate to others that the original scientist is trying to cover something up. The lack of significance of most of the interactions in our study suggests that it even seems as if scientists might already realize this. Therefore, we can generally suggest that scientists admit they are wrong, but only when the evidence suggests they should.

The chart below maps how scientists view others' work (left) and how they suspect others will view their own work (right) if the researcher (the scientist or another, depending on the focus) admitted to engaging in questionable research practices.

Adapted from Fetterman & Sassenberg, "The Reputational Consequences of Failed Replications and Wrongness Admission among Scientists." December 9, 2015, PLOS One.

The primary purpose of the passage is to