Government and behavior
Articles
Year ’23-24
Table of contents
Week 1:......................................................................................................................................................... 2
Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments
reveal some heuristics of thinking under uncertainty. Science, 185(4157), 1124-1131.......................................2
Kunda, Z. (1990). The case for motivated reasoning. Psychological bulletin, 108(3), 480. Until page 490 (stop
at “Does issue involvement lead to unbiased processing?”)................................................................................3
Tummers, L. (2019). Public policy and behavior change. Public Administration Review, 79(6), 925-930. 8........4
Grimmelikhuijsen, Stephan, Jilke, Sebastian, Olsen, Asmus and Lars Tummers. (2017). “Behavioral Public
Administration: Combining Insights from Public Administration and Psychology”. Public Administration
Review, 77(1): 45-56.............................................................................................................................................5
Week 2:......................................................................................................................................................... 7
Barton, A., & Grüne-Yanoff, T. (2015). From libertarian paternalism to nudging—and beyond. Review of
Philosophy and psychology, 6(3), 341-359...........................................................................................................7
De Ridder, D., Feitsma, J. Van den Hoven, M., Kroese, F., ... De Vet, E. (2020).....................................................8
Simple nudges that are not so easy. Behavioural Public Policy, 1-19. doi:10.1017/bpp.2020.36.......................8
Münscher, R., Vetter, M., & Scheuerle, T. (2016). A review and taxonomy of choice architecture techniques.
Journal of Behavioral Decision Making, 29(5), 511-524......................................................................................9
Week 3: Steering bureaucrats....................................................................................................................... 11
Tummers, L. L., Bekkers, V., Vink, E., & Musheno, M. (2015). Coping during public service delivery: A
conceptualization and systematic review of the literature. Journal of Public Administration Research and
Theory, 25(4), 1099-1126...................................................................................................................................11
Andersen, S. C., & Guul, T. S. (2019). Reducing minority discrimination at the front line— Combined survey
and field experimental evidence. Journal of Public Administration Research and Theory, 29(3), 429-444.adm
............................................................................................................................................................................13
Linos, E. (2018). More than public service: A field experiment on job advertisements and diversity in the
police. Journal of Public Administration Research and Theory, 28(1), 67-85.....................................................14
Week 4: Administrative burden.................................................................................................................... 15
Chapter 1 ‘Understanding Administrative Burden’ from Herd, P., & Moynihan, D. P.’s (2019). Administrative
burden: Policymaking by other means. Russell Sage Foundation......................................................................15
Keiser, L. R., & Miller, S. M. (2020). Does Administrative Burden Influence Public Support for Government
Programs? Evidence from a Survey Experiment. Public Administration Review, 80(1), 137-150......................17
Lasky-Fink, J., & Linos, E. (2022). It’s Not Your Fault: Reducing Stigma Increases Take-up of Government
Programs. Available at SSRN 4040234...............................................................................................................18
,Week 1:
- Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics
and Biases: Biases in judgments reveal some heuristics of thinking under
uncertainty. Science, 185(4157), 1124-1131.
Heuristic = heuristic principles which reduce the complex tasks of assessing probabilities and
predicting values to simpler judgmental operations. Heuristics are cognitive shortcuts.
Three types of heuristics:
Representativeness:
This heuristic is used when people judge the probability of an event based on how similar it is
to a prototype or a stereotype. Instead of calculating probabilities, individuals rely on the
perceived similarity of an event to a particular category or stereotype.
Example, if someone encounters a person who is quiet, studious, and wears glasses, they
might assume that this person is a professor, even though many other professions could fit the
description.
Availability:
In this heuristic, people estimate the likelihood of an event based on the ease with which
relevant examples or instances come to mind. If instances of a particular event readily come
to mind, individuals tend to perceive that event as more likely. This can lead to biases because
events that are more vivid, recent, or emotionally charged may be more easily recalled and,
therefore, overestimated in terms of their probability.
Example, if someone has recently read or heard about several shark attacks, they may
overestimate the likelihood of a shark attack when going swimming in the ocean.
Adjustment and anchoring:
This heuristic involves making numerical judgments by starting with an initial value (the
"anchor") and then adjusting it to reach a final estimate. The initial anchor can be arbitrary but
still influences the final judgment. People tend to under-adjust from the anchor, meaning they
don't adjust enough from the initial value.
Example, if you are asked to estimate the cost of a product, and the initial price you see is
very high, your final estimate may still be influenced by that high anchor, even if it's not
reasonable.
Heuristics are valuable tools that simplify complex decision-making, but they come with the
risk of cognitive biases and errors when applied without careful consideration. Tversky and
Kahneman's research has been instrumental in uncovering these biases and understanding
how heuristics can influence our judgments and decisions, contributing significantly to the
fields of psychology and behavioral economics.
, - Kunda, Z. (1990). The case for motivated reasoning. Psychological bulletin,
108(3), 480. Until page 490 (stop at “Does issue involvement lead to unbiased
processing?”)
The discussion is restricted to cases in which motivation can be construed as affecting the
process of reasoning: forming impressions, determining one's beliefs and attitudes, evaluating
evidence, and making decisions.
Accuracy goals lead to the use of those beliefs and strategies that are considered most
appropriate.
- Directional goals lead to the use of those that are considered most likely to yield the
desired conclusion.
Reasoning driven by accuracy goals:
The motivation for accuracy drives individuals to engage in a more thorough and
comprehensive decision-making process, with the ultimate goal of arriving at the most
accurate and well-reasoned judgment or choice possible.
Less fundamental attribution error
It does not always eliminate biases and improve reasoning!
In contrast with:
- Situations where people might rely on shortcuts, biases, or heuristics to make quicker
but potentially less accurate judgments.
Reasoning driven by directional goals:
Mechanisms:
People motivated to arrive at a particular conclusion attempt to be rational and to construct a
justification of their desired conclusion that would persuade a dispassionate observer. They
draw the desired conclusion only if they can muster up the evidence necessary to support it.
- The proposed mechanisms assume that directional goals may influence which beliefs
and rules are accessed and applied on a given occasion.
Decisions are biased by their goals.
Directional goals may lead to more or less lengthy processing under different
circumstances, also allows for the possibility that different goals will lead directly to the
consideration of different beliefs and rules.
Biased accessing of beliefs:
Reasoning driven by directional goals is probably originated from the dissonance tradition,
that has shown that people may bias their self-characterizations when motivated to do so.
- Dissonance research suggests that when people hold conflicting beliefs, attitudes, or values,
they experience a state of psychological discomfort or tension known as cognitive dissonance.
This discomfort motivates individuals to reduce the inconsistency by changing their beliefs,
attitudes, or behaviors.
- Statistical heuristics:
There is evidence that our goals can influence how we use statistical heuristics. Two studies
have shown this, where participants were randomly assigned to conditions, and the changes
made didn't provide any information that could explain the results. In both studies,
participants seemed to use rules only when they aligned with their goals, suggesting a biased
Articles
Year ’23-24
Table of contents
Week 1:......................................................................................................................................................... 2
Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases: Biases in judgments
reveal some heuristics of thinking under uncertainty. Science, 185(4157), 1124-1131.......................................2
Kunda, Z. (1990). The case for motivated reasoning. Psychological bulletin, 108(3), 480. Until page 490 (stop
at “Does issue involvement lead to unbiased processing?”)................................................................................3
Tummers, L. (2019). Public policy and behavior change. Public Administration Review, 79(6), 925-930. 8........4
Grimmelikhuijsen, Stephan, Jilke, Sebastian, Olsen, Asmus and Lars Tummers. (2017). “Behavioral Public
Administration: Combining Insights from Public Administration and Psychology”. Public Administration
Review, 77(1): 45-56.............................................................................................................................................5
Week 2:......................................................................................................................................................... 7
Barton, A., & Grüne-Yanoff, T. (2015). From libertarian paternalism to nudging—and beyond. Review of
Philosophy and psychology, 6(3), 341-359...........................................................................................................7
De Ridder, D., Feitsma, J. Van den Hoven, M., Kroese, F., ... De Vet, E. (2020).....................................................8
Simple nudges that are not so easy. Behavioural Public Policy, 1-19. doi:10.1017/bpp.2020.36.......................8
Münscher, R., Vetter, M., & Scheuerle, T. (2016). A review and taxonomy of choice architecture techniques.
Journal of Behavioral Decision Making, 29(5), 511-524......................................................................................9
Week 3: Steering bureaucrats....................................................................................................................... 11
Tummers, L. L., Bekkers, V., Vink, E., & Musheno, M. (2015). Coping during public service delivery: A
conceptualization and systematic review of the literature. Journal of Public Administration Research and
Theory, 25(4), 1099-1126...................................................................................................................................11
Andersen, S. C., & Guul, T. S. (2019). Reducing minority discrimination at the front line— Combined survey
and field experimental evidence. Journal of Public Administration Research and Theory, 29(3), 429-444.adm
............................................................................................................................................................................13
Linos, E. (2018). More than public service: A field experiment on job advertisements and diversity in the
police. Journal of Public Administration Research and Theory, 28(1), 67-85.....................................................14
Week 4: Administrative burden.................................................................................................................... 15
Chapter 1 ‘Understanding Administrative Burden’ from Herd, P., & Moynihan, D. P.’s (2019). Administrative
burden: Policymaking by other means. Russell Sage Foundation......................................................................15
Keiser, L. R., & Miller, S. M. (2020). Does Administrative Burden Influence Public Support for Government
Programs? Evidence from a Survey Experiment. Public Administration Review, 80(1), 137-150......................17
Lasky-Fink, J., & Linos, E. (2022). It’s Not Your Fault: Reducing Stigma Increases Take-up of Government
Programs. Available at SSRN 4040234...............................................................................................................18
,Week 1:
- Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics
and Biases: Biases in judgments reveal some heuristics of thinking under
uncertainty. Science, 185(4157), 1124-1131.
Heuristic = heuristic principles which reduce the complex tasks of assessing probabilities and
predicting values to simpler judgmental operations. Heuristics are cognitive shortcuts.
Three types of heuristics:
Representativeness:
This heuristic is used when people judge the probability of an event based on how similar it is
to a prototype or a stereotype. Instead of calculating probabilities, individuals rely on the
perceived similarity of an event to a particular category or stereotype.
Example, if someone encounters a person who is quiet, studious, and wears glasses, they
might assume that this person is a professor, even though many other professions could fit the
description.
Availability:
In this heuristic, people estimate the likelihood of an event based on the ease with which
relevant examples or instances come to mind. If instances of a particular event readily come
to mind, individuals tend to perceive that event as more likely. This can lead to biases because
events that are more vivid, recent, or emotionally charged may be more easily recalled and,
therefore, overestimated in terms of their probability.
Example, if someone has recently read or heard about several shark attacks, they may
overestimate the likelihood of a shark attack when going swimming in the ocean.
Adjustment and anchoring:
This heuristic involves making numerical judgments by starting with an initial value (the
"anchor") and then adjusting it to reach a final estimate. The initial anchor can be arbitrary but
still influences the final judgment. People tend to under-adjust from the anchor, meaning they
don't adjust enough from the initial value.
Example, if you are asked to estimate the cost of a product, and the initial price you see is
very high, your final estimate may still be influenced by that high anchor, even if it's not
reasonable.
Heuristics are valuable tools that simplify complex decision-making, but they come with the
risk of cognitive biases and errors when applied without careful consideration. Tversky and
Kahneman's research has been instrumental in uncovering these biases and understanding
how heuristics can influence our judgments and decisions, contributing significantly to the
fields of psychology and behavioral economics.
, - Kunda, Z. (1990). The case for motivated reasoning. Psychological bulletin,
108(3), 480. Until page 490 (stop at “Does issue involvement lead to unbiased
processing?”)
The discussion is restricted to cases in which motivation can be construed as affecting the
process of reasoning: forming impressions, determining one's beliefs and attitudes, evaluating
evidence, and making decisions.
Accuracy goals lead to the use of those beliefs and strategies that are considered most
appropriate.
- Directional goals lead to the use of those that are considered most likely to yield the
desired conclusion.
Reasoning driven by accuracy goals:
The motivation for accuracy drives individuals to engage in a more thorough and
comprehensive decision-making process, with the ultimate goal of arriving at the most
accurate and well-reasoned judgment or choice possible.
Less fundamental attribution error
It does not always eliminate biases and improve reasoning!
In contrast with:
- Situations where people might rely on shortcuts, biases, or heuristics to make quicker
but potentially less accurate judgments.
Reasoning driven by directional goals:
Mechanisms:
People motivated to arrive at a particular conclusion attempt to be rational and to construct a
justification of their desired conclusion that would persuade a dispassionate observer. They
draw the desired conclusion only if they can muster up the evidence necessary to support it.
- The proposed mechanisms assume that directional goals may influence which beliefs
and rules are accessed and applied on a given occasion.
Decisions are biased by their goals.
Directional goals may lead to more or less lengthy processing under different
circumstances, also allows for the possibility that different goals will lead directly to the
consideration of different beliefs and rules.
Biased accessing of beliefs:
Reasoning driven by directional goals is probably originated from the dissonance tradition,
that has shown that people may bias their self-characterizations when motivated to do so.
- Dissonance research suggests that when people hold conflicting beliefs, attitudes, or values,
they experience a state of psychological discomfort or tension known as cognitive dissonance.
This discomfort motivates individuals to reduce the inconsistency by changing their beliefs,
attitudes, or behaviors.
- Statistical heuristics:
There is evidence that our goals can influence how we use statistical heuristics. Two studies
have shown this, where participants were randomly assigned to conditions, and the changes
made didn't provide any information that could explain the results. In both studies,
participants seemed to use rules only when they aligned with their goals, suggesting a biased
