Produced and counterfactual effort contribute to responsibility attributions in collaborative tasks

Xiang, Y., Landy, J., et al. (2023, March 8). 

PsyArXiv

https://doi.org/10.31234/osf.io/jc3hk

Abstract

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the produced effort or force (individuals are more responsible if they produce more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N = 717), we found that participants’ judgments are best described by a model that considers both produced and counterfactual effort. This finding generalized to an independent validation data set (N = 99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

General discussion

Responsibility for the outcomes of collaborations is often distributed unevenly. For example, the lead author on a project may get the bulk of the credit for a scientific discovery, the head of a company may  shoulder the blame for a failed product, and the lazier of two friends may get the greater share of blame  for failing to lift a couch.  However, past work has provided conflicting accounts of the computations that drive responsibility attributions in collaborative tasks.  Here, we compared each of these accounts against human responsibility attributions in a simple collaborative task where two agents attempted to lift a box together.  We contrasted seven models that predict responsibility judgments based on metrics proposed in past work, comprising three production-style models (Force, Strength, Effort), three counterfactual-style models (Focal-agent-only, Non-focal-agent-only, Both-agent), and one Ensemble model that combines the best-fitting production- and counterfactual-style models.  Experiment 1a and Experiment 1b showed that theEffort model and the Both-agent counterfactual model capture the data best among the production-style models and the counterfactual-style models, respectively.  However, neither provided a fully adequate fit on their own.  We then showed that predictions derived from the average of these two models (i.e., the Ensemble model) outperform all other models, suggesting that responsibility judgments are likely a combination of production-style reasoning and counterfactual reasoning.  Further evidence came from analyses performed on individual participants, which revealed that he Ensemble model explained more participants’ data than any other model.  These findings were subsequently supported by Experiment 2a and Experiment 2b, which replicated the results when additional force information was shown to the participants, and by Experiment 3, which validated the model predictions with a broader range of stimuli.

The research is here.

Summary: Effort exerted by each member & counterfactual thinking play a crucial role in attributing responsibility for success or failure in collaborative tasks. This study suggests that higher effort leads to more responsibility for success, while lower effort leads to more responsibility for failure.

The Age Trajectory of Happiness

Kratz, F., & Brüderl, J. (2021, April 18).

https://doi.org/10.31234/osf.io/d8f2z

Abstract

A large interdisciplinary literature on the relationship between age and subjective well-being (happiness) has produced very mixed evidence. Virtually every conceivable age-happiness trajectory has been supported by empirical evidence and theoretical arguments. Sceptics may conclude that the social science of happiness can only produce arbitrary results. In this paper we argue that this conclusion is premature. Instead, the methodological toolbox that has been developed by the modern literature on causal inference gives scholars everything they need to arrive at valid conclusions: the causal inference toolbox only must be applied by happiness researchers. We identify four potential sources of bias that may distort the assessment of the age-happiness relationship. By causal reasoning we derive a model specification that avoids these  biases.  For  an  empirical  illustration,  we  use  the  longest  running  panel  study  with information on happiness, the German Socio-Economic Panel (1984-2017; N persons=70,922; N person-years =565,703). With these data we demonstrate the relevance of the four biases and how combinations of different biases can reproduce almost any finding from the literature. Most biases tend to produce a spuriously U-shaped age trajectory, the most prominent finding from the literature. In contrast, with our specification we find a (nearly monotonic) declining age-happiness trajectory.

Summary and Conclusions

How aging affects happiness is an important research question for the social and behavioral sciences. Our literature review demonstrates that many conflicting age trajectories have been reported in the literature. As this state of research is quite unsettling for the science of happiness, we  discuss—informed  by  recent  advances  in  the  methodology  of  causal  analysis—model specifications used by researchers in this field. Altogether, we identify four main biases that may distort the age trajectory of happiness. By using the German SOEP data, we show that distortions may be huge producing even qualitatively different conclusions. We demonstrate that by using different combinations of mis-specifications it is possible to generate (almost) every trajectory that has been reported in the literature. With a model specification that avoids these four biases, we find an age-happiness trajectory that declines slowly over adulthood (altogether about half a scale point). The decline comes to a halt and we observe even a small increase (about one tenth of a scale point) during the golden ages. Afterwards, in old age a very steep decline in happiness sets in.

The research is here.