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Cognitive pitfalls in dealing with sustainability
Philipp Sonnleitner, Luxembourg Centre for Educational Testing, University of Luxembourg
Main summary points:
•
Environmental conditions in which human ancestors evolved, strongly shaped
human thinking
•
Two complementary cognitive systems can be distinguished whose interplay causes
systematic errors, especially in situations including uncertainty or complexity
•
These cognitive pitfalls have tremendous consequences in the context of
sustainability
•
Leveraging cognitive biases and knowledge about them by improving education
and training is vital to address ecological problems
1. The challenge
The human mind developed to make sense of the world, to structure it, and hence,
control it as far as possible to maximize a human’s chance of survival (Geary, 2005). The
way it works was optimized for the environment in which the genus Homo was evolutionary
shaped, a time period roughly ranging from 2.6 million years ago until 12.000 years ago, also
called the Paleolithic (Wood, 2005). This so called environment of evolutionary adaptedness,
however, is foundationally different from the world of today. Instead of being overwhelmed
by easily available food choices in the local supermarket, early humans were struggling to
secure food at all. Instead of dealing with long term risks of instant pleasures like a decent
meal or smoking a cigarette, they dealt with immediate threats that could only be solved by
quick, physical decisions like running for their life. Instead of living in a complex,
interconnected world, in which every action has (often unintended and unforeseeable) side
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and long-term effects on a local as well as on a global level, small social clans were formed
in which actions had immediate consequences and problems were just locally relevant and
comparatively straightforward. In short, large parts of the brain were optimized for a world
that was completely different from today’s.
This chapter discusses how this cognitive heritage is influencing human decision
making, especially in the context of sustainability. First, the theoretical framework for this
endeavor is set by introducing dual-process models, the current approach in cognitive
psychology to describe human thinking. On base of these models, typical errors in human
decision making, such as framing effects, temporal discounting, or problems in dealing with
complex systems are explored, and linked to the environmental context. This chapter closes
by reinterpreting these systematic errors as part of the solution, and discussing how existing
knowledge about the way human thinking works could be leveraged for solving the numerous
challenges today’s society faces.
2. Human thinking through the lens of cognitive psychology
Phenomena of human decision making are best studied and described by using a
(cognitive) model that formalizes and at the same time structures our conception of how the
brain works. To this end, this chapter adopts the theoretical framework of cognitive
psychology which offers a wealth of studies enlightening the reasons for cognitive fallacies in
everyday decision making and complex problem solving. Mainly drawing on an experimental
approach, cognitive psychology tries to understand and adequately describe how people
perceive information, process it and hence, act upon it (e.g. Solso, 2001). Although resulting
theoretical models can best be understood as approximations of human thinking processes,
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they enable a glimpse into what is going on in the mind and allow for structuring and
predicting complex cognitive performances, such as problem solving, reasoning, or reading.
Current approaches to describe human thinking are called dual-process theories and
mainly distinguish between two cognitive systems (e.g. Evans & Over, 1996; Sloman, 1996;
Stanovich & West, 2000). The first one encompasses so-called “cognitive modules” that
evolved relatively early and enabled individuals to make faster and beneficial choices given
the ecological context they lived in (Geary, 2005). Preferring high caloric, sweet or fat food
that delivers more energy compared to other types of nutrition, would be an example for such
a “cognitive module” (e.g. Beidler, 1982). Since functioning of these modules is largely
unconscious, and automatic, they are often summarized using the umbrella term Autonomous
Set of Systems, Implicit System or System 1 which is optimized to respond to immediate
cues or threats (Evans, 2003; Kahneman, 2011; Stanovich, 2009). The second system, usually
called Analytic System, Explicit System or System 2, in contrast, evolved relatively late; first
indications of it are found in cave drawings from the Upper Paleolithic about 60.000 to
30.000 years ago, demonstrating that some form of abstract thinking was possible, allowing
for symbolizing a bear by an abstract image painted on a cave wall. System 2 encompasses
conscious thinking processes and allows humans to reason, and to plan by extending the time
perspective and mentally simulating cause-and-effect chains. These complex performances,
however, are limited by working memory and are therefore slower than cognitive modules of
System 1. System 2 is therefore more prone to errors and causes a higher metabolism of the
brain which often leads to actions based on “intuitive” suggestions of System 1 instead of
carefully planned outcomes (Evans, 2003; Geary, 2005; Kahneman, 2011; Stanovich, 2009).
Depending on the situation, the two cognitive systems are differentially suited (Geary,
2005). Choosing the most appropriate credit rate is something that should not be decided by
your gut feelings which are product of System 1. Social situations, in turn, might quickly
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become awkward when your vis-à-vis’ body posture, facial expression, and voice intonation
is overthought too much by System 2. Problems in decision making start when situations
have cues that trigger the automatic System 1 but should better be approached by the
conscious System 2, leading to irrational and suboptimal solutions.
2.1. Merits and limitations of the chosen theoretical approach
Especially when talking about psychological phenomena, it has to be kept in mind
that no model is identical with what it aims to describe, but is more like a simplified image of
it. The same applies for cognitive models of human thinking, which often use a highly
mechanistic account and strongly shape the way how we think and do research about certain
topics. Further, a short glance at the history of cognitive psychology shows that such models
mostly are temporary aids to describe certain phenomena of thinking and are modified or
replaced after some decades (e.g. Solso, 2001). Whether dual-process theories stand the test
of time remains to be seen but they face several criticisms, including convincing alternative
approaches, unifying all cognitive processes within one coherent system (cf. Evans &
Stanovich, 2013 or Osman, 2004, 2014).
Interestingly, one reason for the simplicity of existing models to describe the human
mind can be seen in the mind itself. As will be discussed later in the chapter, people have
considerable problems in dealing with complexity, and interpreting or using cognitive models
to explain human behavior is no exception. However, one reason for the development of
System 2 was the need to explain what is going on in the world by building abstract models
that are used to explain and hence, control the environment (Geary, 2005). Thus, applying
somewhat inaccurate but useful working metaphors to guide actions and make predictions is
typically human.
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Note that research on cognitive biases, fallacies, or pitfalls has already had
tremendous impact by disenchanting the myth of humans as economically rational actors
(Kahneman, 2003, 2011). If society strives for a sustainable development, however, it has to
be even more aware of the implications of these cognitive biases on everyday decision
making. Not only do they explain why people behave in an irrational and unsustainable way
in the first place, but knowledge of them also offers possible solutions for avoiding or
counteracting | them in the future. In the following, it will be demonstrated how this
“cognitive architecture” of the brain leads to a variety of fallacies when people are confronted
with decisions.
3. Cognitive fallacies in decision making
3.1.
Prospect theory and framing effects
Depending on whether the focus is set on gains or losses, people opt for different
choices, an (irrational) behavior first explained by Kahneman and Tversky’s (1979) prospect
theory. The credit card industry, for example, made sure that any surcharge for paying with
card was relabeled as discount for paying in cash. Losses are usually perceived to weigh
heavier than gains, therefore paying a surcharge for using a credit card would be much less
attractive than simply to forgo the benefit of paying in cash (see Thaler, 1980). Thus, the way
a problem is framed, that is how a certain issue is introduced and presented, heavily
influences how people see, understand, and decide on that problem (Kahneman, 2003).
Several studies consistently demonstrated that people seek risk when the focus of a problem
is set on sure losses, and avoid risk when the focus is set on gains (see Box 1 for a typical
example). Crucially, this pattern not only affects lay people, but also professionals in their
domain of expertise. In the context of deciding between cancer therapies, McNeil, Pauker,
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Sox, and Tversky (1982) showed that even trained physicians’ choice heavily depended on
the framing of the problem, avoiding risks when presented with sure gains and preferring
risky options when confronted with unavoidable losses.
This discrepancy arises from two characteristics of System 1 (e.g. Stanovich, 2004).
First, System 1 is biased to accept given information and the context or “frame” as is.
Reformulating the initial propositions would be mentally effortful and System 2 would be
needed. Second, the described utility function of preferring secure gains over more promising
but risky options must have been advantageous in the environment of evolutionary
adaptedness, true to the motto “a bird in the hand is worth two in the bush”. Such behavior
absolutely makes sense in an insecure environment in which resources are scarce. Since
System 1 acts automatically and faster than System 2, when reading such scenarios as in Box
1, our gut feelings thus immediately give us a preference for a certain option. Note that when
participants were told to directly compare both scenarios, in other words, System 2 was
activated, all choice preferences between the scenarios vanished. Consequently, it’s not that
people weren’t able to figure out that the scenarios were in fact identical, they just gave the
answer that first came to their mind.
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Box 1 - Engaged Learning Activity: Framing Effects
Read through Scenario 1 and decide which option you would take. Note your decision.
Scenario 1:
Imagine an African village with 600 inhabitants. You would like to ensure power supply
for this village but given the specific circumstances (financial situation, local climate),
there are only two alternatives:
A)
If a diesel generator is purchased, the supply for 200 inhabitants is guaranteed.
B)
If a hydroelectric power production is installed, there is a one-third probability that
600 inhabitants can be provided with energy and a two-third probability that due to a
drought year, nobody has power.
Now read through Scenario 2 and again, note down your decision.
Scenario 2:
Imagine an African village with 600 inhabitants. You would like to ensure power supply
for this village but given the specific circumstances (financial situation, local climate),
there are only two alternatives:
A‘)
If a diesel generator is purchased, 400 inhabitants will be without power supply.
B‘)
If a hydroelectric power production is installed, there is a one-third probability that
everybody will have power supply and a two-third probability that due to a drought
year, 600 inhabitants will be without power supply.
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This example is a typical problem used in research studies on framing effects and identical
concerning the numbers to the example used in Kahneman (2003). Essentially, both scenarios
are exactly the same, only in Scenario 1 focus is set on sure gains which render the uncertain,
risky decision B less attractive whereas in Scenario 2, focus is set on sure losses, making the
riskier decision B‘ more attractive to choose. In fact, this prediction by prospect theory has
been confirmed in a huge amount of studies, reporting a majority of people choosing A in
Scenario 1 and B‘ in Scenario 2.
3.2.
Framing in the environmental context
It has been frequently shown that framing effects also play a central role within the
context of sustainability. For example, Amelung and Funke (2015) investigated opinion
formation concerning climate engineering techniques, such as cloud whitening or injecting
aerosols in the stratosphere to reduce solar radiation reaching earth’s surface, as an alternative
to mitigating CO2 emissions when fighting global warming. They could show that
participants focusing on the risks of current political efforts to mitigate CO2 emissions and
doubting their success, were more likely to adopt climate engineering strategies than
participants reporting more faith in more conservative approaches to the problem. Results
clearly show that the framing of climate engineering techniques as a plan B or back-up
strategy that is usually employed in related research proposals and media coverage, strongly
shapes public opinion on the topic and henceforth steers the debate in a more benevolent
direction towards these techniques (Amelung & Funke, 2015; Bellamy, et al., 2012). Thus,
risk framings, directly targeting certain features of System 1, can create powerful momentum
to steer public and hence political opinion. A prominent example in this context can also be
seen in the German nuclear phase-out in June 2011 that was a direct response to the
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Fukushima Daiichi nuclear disaster that happened 3 months earlier in Japan. Initially, the
then ruling government led by Chancellor Angela Merkel had decided to significantly extend
the operating time for German nuclear power plants only to decide on immediate shut downs
and restrictions of operating time after the incident (Appunn, 2015).
Substantial framing effects were also found concerning the environmental discourse in
the United States that is known to be highly polarized between liberals and conservatives
(Feinberg & Willer, 2013). Liberals’ stronger pro-environmental attitudes (e.g. McCright &
Dunlap, 2011) could largely be explained by the current framing of the environmental debate
in terms of harm and care principles - moral concepts that were found to be more strongly
embraced by liberals than by conservatives. Strikingly, if environmental messages were
reformulated corresponding to moral values of purity and sanctity, differences in proenvironmental attitude between liberals and conservatives completely vanished. One
suggested reason for this shift was that the purity/ sanctity framing referred to moral
principles typically advocated by conservatives, thus triggering System 1 of the conservative
participants to detect familiarity and hence, to put greater trust in the presented message.
These findings not only demonstrate the strong impact of certain framings of environmental
information and messages on public opinion but also highlight that framing effects can be
general but also differential. Since messages or choices cannot be “unframed”, it is especially
important to critically reflect the frame that is used.
3.3.
Temporal discounting and climate change
Risk or uncertainty, are also crucial when thinking about the future. Given the
challenging and unpredictable setting, in which the genus Homo mainly developed, it comes
as no surprise that System 1 is optimized to prefer instant gains over long-term rewards, a
pattern also known as temporal discounting. Within the climate change context, the problem
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is even more aggravated by the fact that potential but uncertain benefits of immediate costs
(e.g. significant reduction of resource consumption) do not only lie in the far future, they only
become perceivable within the next generation(s) at best. Consequently, Jacquet and
colleagues (2013) could impressively show that a common climate protection goal (the
financing of a related newspaper ad), was reached in 70% of the participating groups when
there was an instant (monetary) reward, in 36% of the groups if there was a 7 week delay of
their endowment, and in none of the groups if the common benefit was invested in planting
trees, a proxy for an intergenerational advantage. Especially in the last condition, selfish
behavior of group participants dominated throughout the experiment. According to the
authors, international climate change negotiations won’t succeed until powerful short-term
incentives, such as punishment, reward or reputation will be introduced to the debate, thus
somewhat canceling the effects of temporal discounting.
Compared to stable and secure conditions, predictions for the future become even
more uncertain when the environment is harsh. How this factor additionally influences
decision making, was shown in a recent experiment by Laran and Salermo (2013) in which
participants were primed with either neutral cues or cues of environmental harshness while
having to choose between high-caloric or low-caloric food alternatives. In the environmental
harshness condition, triggering primordial reactions of System 1, participants significantly
chose high-caloric food over low-caloric alternatives, indicating that they (subconsciously)
prepared for tough times by securing high energy food. Crucially, this preference vanished
when neutral cues were presented, strongly suggesting that people adopt different strategies
depending on the perceived environmental conditions. This relation has also been
demonstrated in other studies, showing that the harsher and the more uncertain people
perceive their personal future, the more likely they are to adopt a so-called fast life-history
strategy that is associated with higher consumption, gambling, or a lower age at giving the
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first birth (Nettle, 2010; Griskevicius et al., 2012). In the light of these findings, however, it
seems highly doubtful that current warnings of climate change’s consequences, a looming
scarcity of resources, and a constant environmental alarmism will have their envisaged
effects. Instead, such messages might even increase non-sustainable behavior in that they
trigger cognitive systems that are sensitive to such threats and urge people to think about their
own advantage first.
3.4.
Dealing with problems that are complex
Most problems in the context of sustainability are not isolated but arise within systems
and networks and were thus described as complex problems (see also Chapter 3). Such
problems typically incorporate five characteristics that define them as complex (e.g. Funke,
2001, Dörner, 1989): They (a) consist of a large number of elements that (b) are constantly
influencing each other. The underlying relations (c) include dynamics, for example side
effects or variables that, depending on their state, autoregressively change on their own and
are (d) not transparent to the problem solver. In addition, most scenarios (e) require the
problem solver to achieve several, partly contradictory goals. How these characteristics can
be found in the (complex) problem of applying climate engineering is demonstrated in Box 2.
Basically, interacting with or trying to control complex systems can be seen as a
constant stream of decisions. Since cognitive capacities are limited, the first decision already
starts with choosing where to draw attention within the system. If a problem is identified,
appropriate strategies have to be selected and applied in order to gather knowledge on this
issue, for example how a certain variable (e.g. the application of climate engineering)
influences others (e.g. local temperatures, public opinion on this topic). The impact of
interventions has to be evaluated though, and the decision on the right indicators (e.g. solar
radiation, frequency of extreme weather phenomena) and the right time frame (e.g. months
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vs. years) has to be taken. If finally, it was decided which information is transferred into
knowledge, this understanding has to be purposefully applied and choices concerning the
right targets have to be made. However, since full knowledge of the system’s underlying
connections is mostly impossible as is the definition of goals that are optimal for all involved
stakeholders, complex problem solving can be modelled as a sequence of decisions that are
made under uncertainty (e.g. Osman, 2014). This, however, also implies that all phenomena
discussed above, such as framing effects or prospect theory come into play.
Box 2. Climate engineering as a complex problem?
In an excellent paper on the uncertainties of climate engineering, Amelung and Funke (2013)
illustrate how the decision on injecting aerosols in the stratosphere to reduce solar radiation
and thus counter extreme weather events, can be described using criteria of the complex
problem solving research paradigm:
(a) Complexity: A large number of variables have to be considered for informing this
decision. Besides obvious physical and biological parameters, such as the expected change in
local temperatures and weather conditions and its effects on the prevailing fauna and flora,
also psychological, social, economic, legal, and hence, political aspects have to be
considered. The application of climate engineering techniques might be refused by the public,
but needs sufficient support of political decision-makers who have to negotiate this
supranational issue also with neighboring states. A successful application might have varying
effects on different economic branches (agricultural sector vs. tourism) and benefits may not
outweigh deployment costs and related risks.
(b) Connectivity: The involved variables are part of a system and in a constant
feedback-loop with each other. The prospect of a solution to climate change might lead to a
less responsible consumer behavior neutralizing some of the positive effects. In addition, the
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technique itself, injecting aerosols in the stratosphere, would have several effects, indirect
and probably, due to a lack of case-studies, also unknown ones on variables that are not yet
considered.
(c) Dynamics: Even if the technique would successfully reduce solar radiation, the
impact on temperature and local weather phenomena might not be linear in nature and thus,
be delayed. This, however, may lead to a disappointed public putting increased pressure on
policy makers who could then feel forced to intensify their efforts with even more drastic
interventions.
(d) Intransparency: Although some climate parameters can approximately be
reproduced in computer simulations, understanding the whole (itself complex) climate system
is still beyond reach. Together with unpredictable social impacts, these unknown processes
add to the intransparency of the decision’s consequences.
(e) Polytely. The manifold and various effects of climate engineering would benefit all
involved stakeholders differently. Consequently, multiple and partly contradicting goals have
to be considered and weighted for this decision. Given the large number of involved factions
with contradicting regional interests might render an optimal solution even impossible.
3.5.
Typical errors when dealing with complex systems
Human performance in solving complex problems has been addressed in various
disciplines, ranging from economics to cognitive psychology, using a broad variety of
computer-based simulations that mirror dynamic and non-linear real-word scenarios (for a
thorough overview see Osman, 2010). In general, by interacting with such scenarios over a
certain amount of time, people could learn to control these systems reasonably well, with
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knowledge on the underlying connections being the key aspect (e.g. Beckmann & Goode,
2010; Sonnleitner, Brunner, Keller, & Martin, 2013; Osman, 2014). But research also
revealed a significant amount of typical errors that are made.
If you know nothing about a system, the first step is to explore its underlying
mechanics in order to build a mental model of how the variables are linked to each other. But
where to turn first and how to find out? Most studies have revealed, that people don’t apply a
systematic way to gather knowledge, probably because no hypotheses are formulated and
hence tested (Dörner, 1989). Partly, this could be explained with the lack of clear goal setting
during this initial exploration phase. With the diffuse and uncertain aim of finding out how
the scenario works, the mind looks for familiar anchors and System 1 “intuitively” suggests
topics or problems that fit this criteria. Beckmann and Goode (2014) found out, however, that
such “false familiarity” – vague but in no way perfect knowledge that is triggered by the
semantic meaning of a certain system variable – has detrimental effects on knowledge
acquisition. Existing assumptions are taken as correct and are not systematically tested, thus
leading to a faulty or incomplete mental model of the scenario. The missing of a deliberately
planned exploration strategy and the subsequent reliance on familiarity also leads to so-called
“thematic vagabonding” and the focus on single goals instead of a necessary holistic
problem-solving approach (Brehmer, 2005). This fragmentary system exploration is
aggravated by the tendency to overestimate the status quo of a system – in other words, the
information that is available right now – and overlook feedback delays or future side effects
and tipping points of their interventions. Again, in a relatively uncertain environment, System
1’s predisposition of a myopic time perspective may be advantageous, but not in the long run.
A recent study by Sonnleitner, König, and Sikharulidze showed that students within a
course on sustainable development and social innovation had problems to interpret
information created while exploring a problem correctly. Effects of an invisible system
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variable were either completely attributed to other visually represented system elements or
accounted for twice by simply not differentiating them from other visible effects. Reasons for
these errors were seen in a complex interaction of overemphasizing visually represented
information (a feature of System 1) and the high cognitive workload to disentangle two
sources of an effect (limitations of System 2). These findings also point to the high impact of
reasoning ability on complex problem solving performance. Studies showed that even full
information about the underlying connections does not guarantee perfect achievement of
envisaged targets; it depends on what you make out of this knowledge (e.g. Goode &
Beckmann, 2010; Sonnleitner, Brunner, Keller, Martin, 2013). Other mental pitfalls that were
found in this phase of knowledge generation are mistaking correlations with causation, the
illusion of being able to control variables that are beyond the influence of the problem solver,
and the preference to look for evidence that supports the preconceived model instead of
disproving it, which would be mentally more demanding (Brehmer, 2005; Dörner, 1989).
Taken together, people were found to apply suboptimal strategies to generate information on
unknown systems and in addition, build wrong mental models by drawing wrong conclusions
about this information either because of mental limitations to understand the generated
information or because of mental shortcuts provided by System 1. Thus, the basis for
systemic interventions in most cases is already flawed.
Since problem solvers are in a constant feedback loop with the systems they interact
with, they can learn from their mistakes, adapt their faulty mental models, and reasonably
reach their envisaged targets (Brehmer, 2005, Osman, 2014). Frequently, however, this
feedback loop ends in a downward spiral, especially when due to wrong knowledge,
interventions have unexpected outcomes, and hence increase uncertainty and emotional
pressure to solve the situation. Typical reactions to reduce these negative emotions range
from rash, pointless actions to extensive and paralyzing information seeking to fill in the
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existing knowledge gaps (Dörner, 1989). Even if meaningful interventions are made, their
impact could be delayed due to an exponential change rate or initially neutralizing sideeffects. Most people, however, expect and act upon linear changes and therefore increase
their activities in the absence of estimated effects. The focus on present states and the neglect
of processes underlying a certain system, finally, leads to an oscillation of actions causing a
constant over- and undershooting of targeted values.
4. Leveraging insights from cognitive psychology
4.1.
Informing and “nudging” the public
In the light of the reviewed examples, it seems evident that cognitive biases play a
crucial role in the context of sustainability. But what can be done to lessen their impact or
prevent them in the first place? One important step is raising awareness of cognitive biases in
the broad public (for an excellent example see Marshall, 2014) and disciplines other than
(cognitive) psychology. With the introduction of behavioral economics to many curricula
focusing on finance and economy for example, this slowly gets momentum. Since
environmental issues can only be tackled by a multidisciplinary effort, knowledge on human
decision making should also be part of other disciplines that train students for acting within
complex systems (e.g. engineering, politics, or spatial planning).
Another route is the explicit consideration of cognitive biases in policy making and
the design of so-called decision architectures, as already done in some contexts (Thaler &
Sunstein, 2008). founIn fact, for several years governments have been exactly to do this, by
applying a soft paternalism that “nudges” people in a certain direction when confronted with
decisions (for a detailed discussion of this account and examples see Thaler and Sunstein,
2008). However, despite being promising, this approach has been empirically challenged and
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is far away from being the key to all sustainability problems (Osman, 2014, 2015). It has to
be complemented by a stronger inclusion of the wider public through a social dialogue,
otherwise such “nudging” will provoke reactance in people, the unwillingness to comply with
actually beneficial measures, just because behavioral freedom is seemingly threatened
through their introduction (e.g. Miller, Lane, Deatrick, Young, & Potts, 2007). Instead of
patronizing the public, informing it on an equal footing about existing mental pitfalls,
showing them and conjointly developing ways to face uncertainty by avoiding such fallacies
and having faith that people take on this responsibility, may not only be more mature but also
more likely to succeed (Osman, 2014). An idea that Ravetz (2006) conceived as post-normal
science.
4.2.
Adapting educational curricula
One consequence of studying human decision making in complex environments, was
the request for adapting educational curricula by explicit training of systems thinking using
computer-based scenarios (Dörner, 1989; Vester, 2012). People should not only be informed
about cognitive pitfalls in dealing with complex problems but should make their own
experiences in order to learn from them. The question arises, however, how the training
simulations should be designed in order to maximize the learning outcome. Should they
reflect real, specific scenarios or be formulated in a more abstract, general way? Funke
(2006) highlighted the discrepancy in this context that the more detailed scenarios are, the
more situation-specific and therefore useless for other scenarios the acquired knowledge will
be. On the other hand, if very general and abstract scenarios are used for training, domaingeneral principles might be demonstrated but their usefulness for domain-specific situations
might be questionable. In addition, Beckmann and Goode (2014) pointed out that scenarios
which use meaningful labels for the included variables, thus being “semantically embedded”,
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might hamper learning about the underlying connections and dynamics since learners might
have false prefixed associations with the scenario’s variables that are not tested
systematically (see above). They consequently suggest that novel or abstract contexts might
therefore be advantageous for learning general skills like systematically exploring relations
between variables. If semantically embedded scenarios have to be used in order to teach
specific knowledge, essential steps of the training would be the explication and hence, the
consequent and systematic test of learners’ assumptions about the system. Beckmann and
Goode also highlight that if concrete goals are given in the phase of exploring a system,
people aim to reach them simultaneously by manipulating several input variables at the same
time. Needless to say that such a mix of interventions makes it impossible to draw clear cut
conclusions about cause-and-effect chains and thus, significantly reduces the gathered
knowledge. The interventions might have been successful but the learner does not know why,
hence does not develop a secure foundation of understanding the system and finally, develops
(unjustified) overconfidence in his problem solving skills. From this point of view, existing
computer-based simulations aiming to teach systems thinking, such as Vester’s Ecopolicy
(2011) that asks the learner to solve several problems of a fictitious state (e.g. poor
educational level and health conditions of the population, high environmental pollution, or a
faltering economy) within a few steps, might not reach their goals.
Studies on the training of systems thinking and complex problem solving are still
scarce and produced mixed results. Krétzschmar and Süß (2015) showed that university
students were able to extract general principles of system exploration by just interacting with
several, heterogeneous scenarios over a certain amount of time. Although they outperformed
peers without training in gathering knowledge about a new scenario, they could not use this
advantage in reaching specific target values. Micheli (2016) used another approach to teach
complex problem solving, by discussing characteristics of complex problems and its
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implications for interacting with them within a one hour lecture and afterwards assessing
students’ problem solving skills. However, the intervention showed almost no effect. A
successful training approach was reported by Akcaoglu, Gutierrez, Hodges, and Sonnleitner
(2016). They found that students enrolled in a course on how to design and basically program
computer games significantly improved their performance to interact with a complex problem
solving scenario, thus pointing to the possibility that also indirect training methods might be
efficient. An approach in the context of sustainability education was recently presented by
Sonnleitner, König, and Sikharulidze (under review), who confronted learners with a
gamified problem-solving scenario of reduced complexity. While working on the problem,
students took notes about experienced emotions which were then discussed within a lecture
focusing on cognitive pitfalls in dealing with complexity. Data revealed that typical problems
and reactions observed in more complex and larger scenarios (as discussed above) could be
replicated even in this simpler simulation, and hence used for increasing the awareness and
sensitivity of learners for their own mistakes and limitations. Exercises to reflect, for
instance, on framing effects, such as the one presented in Box 1 could additionally help to
make these phenomena more tangible.
The need for sensitizing students to cognitive biases and for training them in systems
thinking on a larger scale, even by integrating these aspects into educational curricula, is
beyond doubt, given its importance (outlined in Chapter 3) and by the frequent, severe
mistakes people make when learning and applying it. Recent efforts to include skills relevant
for systems thinking in educational curricula (e.g. Micheli, 2016; OECD, 2014) and curricula
on sustainable development (Sonnleitner, König, & Sikharulidze, under review) paint a
promising picture. But a clear and convincing concept of how to teach systems thinking and
which tools to use for it, is still missing. For the moment, informing about and demonstrating
typical cognitive biases through experience using simulations in this context, seems to be a
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solid foundation for further developments. Only if decision-makers face their own limitations
in the light of uncertainty, can they appreciate the complex nature of interacting with systems
and avoid some of the mistakes mentioned above.
5. Conclusion
The present chapter set out to highlight the influence of humans’ cognitive heritage on
today’s intricate decisions, especially in the context of sustainable development. Although in
the reviewed examples, this heritage seems more like a burden than a rich inheritance, history
has shown that overcoming barriers by creatively solving problems using newly invented
tools and techniques is at the core of the human species. In this sense, utilizing insights of
(the tool) cognitive psychology and orchestrating it with other techniques reviewed
throughout this book might be a promising path to solve the problems of sustainability in the
best tradition of mankind.
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Questions for comprehension & reflection:
•
Why are spontaneous preferences for certain decisions sometimes suboptimal?
•
What could be unintended consequences of campaigns against global warming
emphasizing that society is running out of time to make a change?
•
What characterizes complex problems and what problems do people have in
dealing with them?
•
Observe yourself the next time you go grocery shopping. How many of your
purchase decisions do you take consciously, how many of them are based on
spontaneous impulses?
•
Imagine, your local supermarket reorganizes its shelves and at the place of your
favorite chocolate bar, you find a low calorie alternative. A bit irritated, you find
your preferred one at the very bottom of the shelf that is hard to reach. How would
you feel about this “nudging” towards healthier alternatives?
•
What could be done to better prepare executives to deal with complex problems and
what are potential reasons for resistance?
Core readings:
•
Brehmer, B. (2005). Micro-worlds and the Circular Relation between People and
their Environment. Theoretical Issues in Ergonomics Science 6 (1), pp. 73–93.
•
Kahneman, D. (2003). A Perspective on Judgment and Choice: Mapping Bounded
Rationality. American Psychologist 58 (9), pp. 697–720.
•
Marshall, G. (2014) Don't Even Think About It: Why Our Brains Are Wired to
Ignore Climate Change. Bloomsbury USA.
•
Vester, F. (2012). The Art of Interconnected Thinking: Ideas and Tools for tackling
with Complexity. Munich: MCB-Verlag
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6. References
Akcaoglu, M., Gutierrez, A.P., Hodges, C.B. & Sonnleitner, P. (2017). “Game Design as a
Complex Problem Solving Process”, in Zheng, R. & Gardner, M. K. (eds.) Handbook of
Research on Serious Games for Educational Applications. Hershey, PA: IGI Global, pp.
217-233
Amelung, D. & Funke, J. (2013). Dealing with the Uncertainties of Climate Engineering:
Warnings from A Psychological Complex Problem Solving Perspective. Technology in
Society 35 (1), pp. 32-40. doi: 10.1016/j.techsoc.2013.03.001
Amelung, D. & Funke, J. (2015) Laypeople's Risky Decisions in the Climate Change
Context: Climate Engineering as a Risk-Defusing Strategy? Human and Ecological Risk
Assessment 21 (2), pp. 533-559. doi: 10.1080/10807039.2014.932203
Appunn, K. (2015). The History Behind Germany's Nuclear Phase-out. Clean Energy Wire
Factsheet. [online] URL: https://www.cleanenergywire.org/factsheets/history-behindgermanys-nuclear-phase-out
Beckmann, J. F. & Goode, N. (2014). The benefit of being naïve and knowing it: the
unfavourable impact of perceived context familiarity on learning in complex problem
solving tasks. Instructional Science 42(2): 271-290
Beidler, L.M. (1982). “Biological Basis of Food Selection”, in Barker, L.M (ed.) The
Psychology of Human Food Selection, Chichester, UK: England Ellis Horwood Limited,
pp.3-15
Bellamy, R., Chilvers, J., Vaughan, N. & Lenton, T. (2012). A Review of Climate
Geoengineering Appraisals. WIREs Climate Change 3(6), pp. 597-615. doi:
10.1002/wcc.197
22
Cog pitfalls 170312 JR
Brehmer, B. (2005). Micro-worlds and the Circular Relation between People and their
Environment. Theoretical Issues in Ergonomics Science 6 (1), pp. 73–93. doi:
10.1080/14639220512331311580
Dörner, D. (1989). Die Logik des Misslingens: Strategisches Denken in Komplexen
Situationen. [The Logic of Failing: Strategic Thinking in Complex Situations. Hamburg:
Rowohlt
Evans, J. St. B. T. (2003). In Two Minds: Dual-process Accounts of Reasoning. Trends in
Cogntive Science 7, pp. 454–459
Evans, J. St. B. T. & Stanovich, K. E. (2013). Dual-Process Theories of Higher Cognition:
Advancing the Debate. Perspectives on Psychological Science 8 (3), pp. 223-241.
Evans, J. St. B. T. & Over, D.E. (1996). Rationality in the Selection Task: Epistemic Utility
versus Uncertainty Reduction. Psychological Review 103(2), pp. 356-363. doi:
10.1037/0033-295X.103.2.356
Feinberg, M. & Willer, R. (2013). The Moral Roots of Environmental Attitudes.
Psychological Science 24 (1), pp. 56-62. doi: 10.1177/0956797612449177
Funke, J. (2001). Dynamic Systems as Tools for Analysing Human Judgement. Thinking and
Reasoning 7 (1), pp. 69-89. doi : 10.1080/13546780042000046
Funke, J. (2006). “Komplexes Problemlösen [Complex Problem Solving]” in Funke, J. &
Birbaumer, N. (eds.) Denken und Problemlösen. Enzyklopädie der Psychologie.
Göttingen: Hogrefe, pp. 375-446
Geary, D.C. (2005). The Origin of Mind: Evolution of Brain, Cognition, and General
Intelligence. Washington, DC: American Psychological Association
Goode, N. & Beckmann, J.F. (2010). You Need to Know: There is a Causal Relationship
between Structural Knowledge and Control Performance in Complex Problem Solving
Tasks. Intelligence 38 (3), pp. 345-352. doi: 10.1016/j.intell.2010.01.001
23
Cog pitfalls 170312 JR
Griskevicius, V., Ackerman, J.M., Cantú, S.M., Delton, A.W., Robertson, T.E., Simpson,
J.A., Thompson, M.E. & Tybur, J.M. (2013). When the Economy Falters, Do People
Spend or Save? Responses to Resource Scarcity Depend on Childhood Environments.
Psychological Science 24 (2), pp. 197-205. doi: 10.1177/0956797612451471
Jacquet, J., Hagel, K., Hauert, C., Marotzke, J., Röhl, T., & Milinski, M. (2013). Intra- and
intergenerational discounting in the climate game. Nature Climate Change 3:1025–1028.
Kahneman, D. & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk.
Econometrica 47(2), pp. 263-291. doi: 10.2307/1914185
Kahneman, D. (2003). A Perspective on Judgment and Choice: Mapping Bounded
Rationality. American Psychologist 58 (9), pp. 697–720. doi:10.1037/0003-066X.58.9.697
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux
Kretzschmar, A. & Süß, H.M. (2015). A Study on the Training of Complex Problem Solving
Competence. Journal of Dynamic Decision Making 1 (4), pp. 1-15.
doi:10.11588/jddm.2015.1.15455
Laran, J. & Salerno, A. (2013). Life-History Strategy, Food Choice, and Caloric
Consumption. Psychological Science 24, pp.167-173. doi: 10.1177/0956797612450033
Marshall, G. (2014) Don't Even Think About It: Why Our Brains Are Wired to Ignore
Climate Change. Bloomsbury USA.
McCright, A. M., & Dunlap, R. E. (2011). The politicization of climate change: Political
polarization in the American public’s views of global warming. The Sociological
Quarterly, 52, 155–194.
McNeil, B.J., Pauker, S.G., Sox, H.C. Jr. & Tversky, A. (1982). On the Elicitation of
Preferences for Alternative Therapies. New England Journal of Medicine 306(21), pp.
1259-1262.
doi: 10.1056/NEJM198205273062103
24
Cog pitfalls 170312 JR
Micheli, E. G. (2016). Complex Problem Solving and the Theory of Complexity in High
School Teaching. Unpublished Dissertation. University of Bergamo, Italy.
Miller, C.H., Lane, L.T., Deatrick, L.M., Young, A.M. & Potts, K.A. (2007). Psychological
Reactance and Promotional Health Messages: The Effects of Controlling Language,
Lexical Concreteness, and the Restoration of Freedom. Human Communication Research
33 (2), pp. 219-240. doi: 10.1111/j.1468-2958.2007.00297.x
Nettle, D. (2010). Dying Young and Living Fast: Variation in Life History across English
Neighborhoods. Behavioral Ecology 21 (2), pp. 387-395. doi: 10.1093/beheco/arp202
OECD (2014). PISA 2012 results: Creative problem solving: Students' skills in tackling reallife problems (Volume V). PISA, OECD Publishing
Osman, M. (2004). An Evaluation of Dual-process Theories of Reasoning. Psychonomic
Bulletin & Review 11(6), pp. 988-1010. doi:10.3758/BF03196730
Osman, M. (2010). Controlling Uncertainty: Decision Making and Learning in Complex
Worlds. Chichester: Wiley-Blackwell.
Osman, M. (2014). Future-Minded: The Psychology of Agency and Control. Basingstoke:
Palgrave MacMillen.
Osman, M. (2015). Does our unconscious rule? Psychologist 28 (2), 114-117.
Ravetz, J.R. (2006). Post-normal Science and the Complexity of Transitions towards
Sustainability. Ecological Complexity 3 (4), pp. 275–284. doi:
10.1016/j.ecocom.2007.02.001
Sloman, S.A. (1996). The Empirical Case for Two Systems of Reasoning. Psychological
Bulletin 119 (1), pp. 3-22. doi: 10.1037/0033-2909.119.1.3
Solso, R. L. (2001). Cognitive Psychology. Boston: Allyn and Bacon
25
Cog pitfalls 170312 JR
Sonnleitner, P, König, A. & Sikharulidze, T. (under review). Computer-based Complex
Problems as a Tool to Illustrate Systems Thinking and Confront Learners with Emotions
and Uncertainty When Facing Complexity. Environmental Education Research, pp. 1-44
Sonnleitner, P., Keller, U., Martin, R. & Brunner, M. (2013). Students’ Complex Problemsolving Abilities: Their Structure and Relations to Reasoning Ability and Educational
Success. Intelligence 41 (5), pp. 289–305. doi:10.1016/j.intell.2013.05.002
Stanovich, K. E. (2004). The Robot’s Rebellion: Finding Meaning the Age of Darwin.
Chicago: University of Chicago Press
Stanovich, K. E. (2009). What Intelligence Tests Miss: The Psychology of Rational Thought.
Yale: University Press.
Stanovich, K.E. & West, R.F. (2000). Individual Differences in Reasoning: Implications for
the Rationality Debate? Behavioral and Brain Sciences 23, pp. 645-726
Thaler, R.H. & Sunstein, C.R. (2008). Nudge: Improving Decisions about Health, Wealth,
and Happiness. New Haven, London: Yale University Press
Thaler, R.H. (1980). Toward a Positive Theory of Consumer Choice. Journal of Economic
Behavior and Organization 1(1), pp. 39-60. doi: 10.1016/0167-2681(80)90051-7
Vester, F. (2012). The Art of Interconnected Thinking: Ideas and Tools for tackling with
Complexity. Munich: MCB-Verlag
Wood, B. (2005). Human Evolution: A Very Short Introduction. New York: Oxford
University Press
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