APPLIED COGNITIVE PSYCHOLOGY
Appl. Cognit. Psychol. 22: 559–572 (2008)
Published online 27 June 2007 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/acp.1381
Beliefs About Interactions between Factors in the
Natural Environment: A Causal Network Study
PETER A. WHITE*
School of Psychology, Cardiff University, Wales, UK
SUMMARY
This research is concerned with the structure of people’s beliefs about causal processes in complex
natural systems. A set of entities was derived from an expert analysis of important factors in relation
to forest ecosystems and climate change. These included human population, atmospheric carbon
dioxide levels, fires and several biological features such as extinction rates. In two experiments
participants were presented with each pair of entities and asked whether change in one would produce
change in the other. From the judgements made, causal networks were constructed that reflected
consensual causal beliefs. The resultant causal network was unidirectional, with some entities, such
as humans, functioning as causal origins and others, such as extinction rates, functioning as effects.
This is consistent with previous research showing unidirectional patterns of thinking about causality
in natural systems. Copyright # 2007 John Wiley & Sons, Ltd.
Concern about the continuing destruction of the world’s ecosystems, climate change and
species loss has never been greater than it is now. Habitat destruction and overfishing are
probably causing extinctions on a scale unparalleled in historical times as well as profound
modifications to food webs and other aspects of ecosystems. The world as a whole is
rapidly becoming warmer, and other more localised but equally significant changes in
climate appear to be occurring. There is a widespread belief that human activities,
particularly those that influence atmospheric concentrations of greenhouse gases such as
carbon dioxide and methane, are contributing significantly to these changes.
These events and trends affect everyone in some way and to some degree. At the same
time the processes are of immense complexity, not only because of the vast numbers of
entities involved but also because interactions between those entities, even within quite
small and localised food webs, are usually too complex for their outcomes to be predictable
(Buchanan, 2002; Pimm, 1982; Ricklefs, 1993; Yodzis, 2000). Different experts on climate
change and biodiversity have different beliefs about what the future is likely to hold and
about the causal importance of various factors in determining trends such as extinction
rates (Morgan, Pitelka, & Shevliakova, 2001). Non-experts, which is to say almost
everyone, are likely to have beliefs about the roles of various factors and their probable
effects, but in the face of the vast complexity of the subject matter, the sometimes
conflicting information communicated by the mass media, and the general uncertainty
*Correspondence to: Peter A. White, School of Psychology, Cardiff University, Tower Building, Park Place,
Cardiff CF10 3AT, Wales, UK. E-mail: [email protected]
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P. A. White
about how natural systems function on global scales, such beliefs will almost inevitably be
oversimplified and inaccurate, not to say partial. Since ordinary people, theoretically at
least, can influence the decisions of relevant policy makers by the force of their opinions
and values, there is an obvious practical importance in ascertaining the content and
structure of lay beliefs about causal processes related to such matters as extinction and
climate change.
The focus of this study is not on isolated individual causal beliefs, such as that increase in
the concentration of carbon dioxide in the atmosphere tends to produce global warming,
but on how individual causal beliefs are related in an overall causal structure and the form
of that structure. The importance of ascertaining naive causal structures is that they convey
consensual ideas about how given domains function as systems of interconnected parts. For
example, people may believe that overfishing causes catastrophic decline in the cod
population, but knowing that people believe this carries very little meaning unless the
individual belief can be placed in a network of beliefs about entities related to fishing and
cod. People might believe that decline in the cod population causes a corresponding decline
in the populations of natural predators of cod, as well as a corresponding increase in
whatever cod normally eat and that these changes would support a rapid recovery in the cod
population. If this is their causal structure of the cod ecosystem, then people may believe
that overfishing has only short-term effects, instead of causing lasting disruption to the
ecosystem of which cod are a part.
Not only that, but causal structures can have various kinds of implicit organisation that
function as general ideas about how natural systems function. For example, people might
tend to construct natural systems as interactive systems in which equilibria are maintained
by processes of negative feedback. In fact, the evidence from research on naive ecology is
that people only understand negative feedback processes in simple systems of two or three
entities (Green, 1997). With more entities people construct natural systems in ways that
involve unidirectional causal influence (White, 1995), and they reason about natural
systems as if they were simple unidirectional systems. In particular, if asked to judge the
effects of a perturbation to one entity in a food web, people consistently judge that
the greatest effects will be found for species immediately adjacent to the perturbed entity
in the structure of the food web and that the magnitude of the effect rapidly drops off with
increasing distance from the perturbation (White, 1997, 1998, 1999, 2000). This reveals a
general failure to appreciate the interactive processes that govern the operations of natural
systems (Pimm, 1982; Ricklefs, 1993).
Causal structures can be ascertained by a method called causal network analysis.
Participants are presented with all possible pairs of a set of entities in a defined system, and
they are asked whether or not one member of the pair could be a cause of the other, or
whether change in one member would affect the other. Causal network analysis then
identifies a consensual representation, a structure that incorporates all or most of the
entities and indicates the most commonly endorsed links between them. Causal network
analysis has been used to identify causal structures in several domains, including crime
(Campbell & Muncer, 1990), unemployment (Green, McManus, & Derrick, 1998), poverty
(Heaven, 1994), examination failure (Lunt, 1988), loneliness (Lunt, 1991), personal debt
(Lunt & Livingstone, 1991), terrorist attacks (Reser & Muncer, 2004) and heart attacks
(French, Marteau, Senior, & Weinman, 2002). However, only one previous study has
investigated the causal structure of beliefs about the natural environment (White, 1995).
In that study the entities comprised four parameters of human activity (population,
amount of farmland, use of insecticide, use of fertilizer), four parameters relating to plants
Copyright # 2007 John Wiley & Sons, Ltd.
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(amount of forest, amount of grassland, amount of desert and number of species of plants)
and four categories of animals (populations of, respectively, carnivores, herbivores and
insects and number of species of animals). These entities were selected in part because
most of them could be related in a schematic food web: carnivores tend to eat herbivores
and herbivores eat plants, for example. Participants were asked to imagine a large area of
the environment, consisting of roughly equal areas of forest, grassland and farms and some
waste area (desert). On each page of the questionnaire a specific change (increase or
decrease) to one entity was specified, and participants judged whether this would result in
increase, decrease, or no change in each of the other entities.
The network constructed from participants’ responses had two striking features. One was
that it was predominantly unidirectional. Tracing a route through the network from any
starting point almost invariably led to a terminus, an entity consensually judged not to
cause any further change. The only feedback loop in the network was between a decrease in
the population of herbivores and a decrease in the number of animal species. The other
main feature was that human population exerted a pervasive control over other entities but
was not affected by any other entity. Increase in human population was judged as directly
producing increases in the area of farmland and the use of insecticide and fertilizer, and a
decrease in the area of forest, and these in turn led to changes in all the other entities except
the area of grassland. Decrease in human population was judged as directly producing
decreases in the area of farmland and the use of fertilizer, and increases in the area of forest,
the number of animal species, and the population of carnivores, and these in turn led to
changes in all the other entities except the area of grassland.
However, these findings serve to illustrate one potential weakness of causal network
analysis, that the network obtained and its structural features depend on the entities
selected for study. Clearly there are many possibly relevant factors that were not included
in the study by White (1995), such as fires, diseases and change in the level of carbon
dioxide in the atmosphere. It is not certain that linear structures dominated by human
population and human activities would result from causal networks involving these and
other possible entities.
One solution to the problem of deciding which entities to include in a causal network
analysis on an ecological issue would be to rely on expert assessments of the relative
importance of different factors. A problem with this is that expert judgements vary across
individuals, and in ways that tend to be underestimated by some methods of eliciting expert
opinion (Morgan et al., 2001). Morgan et al. (2001) used a structured interview to elicit
expert judgements on a variety of matters concerned with northern (temperate) forests.
Issues included the impact of a doubling of the atmospheric level of carbon dioxide,
migration rates (i.e. migration of the forest in relation to climate change) and extinction
rates. Experts were not asked to construct causal networks, nor were judgements elicited in
such a way that causal networks could be readily derived from them. However, the method
did result in a broad consensus on the importance of several factors in relation to northern
forest ecology, and these formed the basis for selection of entities for the present study.
The different studies of causal networks have used different methods to elicit causal
judgements and to construct the resultant network. The present study followed French et al.
(2002) and White (1995) in using an elicitation procedure that required participants to
consider every possible combination of entities. In a written questionnaire, an entity was
identified at the top of each page and under it all the remaining entities were listed.
Participants simply endorsed every entity that they thought would be affected by a change
in the entity at the top of the page. This procedure has the advantage that it does not rely on
Copyright # 2007 John Wiley & Sons, Ltd.
Appl. Cognit. Psychol. 22: 559–572 (2008)
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P. A. White
recall of causal beliefs. As Lunt (1988) pointed out, one potential drawback is that
participants may endorse large numbers of links simply because they could imagine that
there could always be some unusual circumstance under which a given link might occur.
However, the procedure for constructing the causal network selects only links endorsed by
a large proportion of participants, so it is unlikely to include those that are regarded as
linked only by unusual circumstances.
The usual criterion for inclusion of links in a causal network is the minimum systems
criterion (MSC). In the MSC the link that has been endorsed by most participants is
selected first, then the second most popular link and this continues until all entities have
been included in the network. The resultant causal network effectively embodies an
assumption that all the entities form a single system. If this is not the case, then an empirical
indication of this can be found by plotting number of links against number of endorsements
per link. An entity is not part of the system if adding that entity to the network incurs a large
cost in terms of the number of other links that must be added. Any such entity is then
excluded from the network.
EXPERIMENT 1
Method
Participants
Seventy-eight first year undergraduate students of psychology participated in return for
course credit. Two participants were excluded prior to data analysis because they had
omitted several judgements, so n ¼ 76.
Materials
The materials consisted of a written questionnaire with an instruction sheet followed by 11
pages of judgemental tasks.
The instruction sheet asked participants to think of a large area of northern temperate
forest, about 100 square miles. They were told that, on each of the succeeding pages, they
would see, at the top and underlined, some change described. Under that would be listed
several other things that might or might not change as a result of the change described at the
top of the page. They were told that their task was to judge whether the change described at
the top of the page would bring about a change in each of the things listed below it or not.
To make their judgement, they should just put ‘Y’ for yes or ‘N’ for no.
Each page of the rest of the questionnaire had, at the top and underlined, some change
identified (e.g. ‘change in the level of carbon dioxide in the atmosphere’). Under this was
written, ‘Please judge whether or not this would bring about a change in each of the
following’, and under that the remaining entities were listed.
Based on the factors discussed by the experts in the study by Morgan et al. (2001), the
following list of entities was constructed:
The level of carbon dioxide in the atmosphere [CO2].
The extent or total area of the forest [extent].
The amount of hunting that goes on [hunting].
The rate at which new animal species are introduced to or enter the area [introduction].
Climate [climate].
The rate of extinction of animal species [extinction].
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The frequency and severity of fires [fires].
Levels of nutrients in the soil [nutrients].
Prevalence of pests and diseases [disease].
The level of air pollution [pollution].
The level of human population [humans].
Terms in parentheses are the identification labels for the entities that will be used in the
remainder of this paper. They were not included in the materials given to participants. The
entities were designed to reflect factors discussed by experts in relation to climate change
impact on forest ecosystems (Morgan et al., 2001) while avoiding terminology that might
not be meaningful to non-expert participants. The order of entities in the list was
randomised, and the order of pages in the questionnaire was randomised independently for
each participant.
Procedure
Participants took part individually or in groups of two or three in a large office. If in groups,
participants were positioned so that none could see what the others were doing. The
materials for this experiment were included with those for two other experiments on
unrelated topics. Participants were supervised by an experimenter who introduced the
experiments, handed out informed consent forms and invited participants to ask questions
if anything in the instructions was not clear. There were no questions concerning the
present experiment. At the end, participants were thanked and given course credit and a
debriefing sheet which explained the aims of the research.
Results
The full matrix of endorsement frequencies (out of a total of 76 responses in each cell) is
presented in Table 1. There was evidently a high degree of consensus about several causal
links, with nine links being endorsed by at least 70 of the 76 participants. Two links were
endorsed by all participants but one. One was a link from pollution to CO2. It therefore
appears to be a widespread belief that change in levels of air pollution affects the level of
Table 1. Endorsement frequencies, Experiment 1
Effect
Cause
1
2
3
4
5
6
7
8
9
10
11
1
2
3
4
5
6
7
8
9
10
11
—
62
9
39
52
34
70
42
25
75
66
35
—
31
35
41
20
74
54
30
34
57
9
55
—
70
24
70
41
9
39
7
69
29
62
70
—
51
71
62
45
62
37
49
64
40
4
16
—
8
39
16
9
63
36
32
56
75
66
55
—
66
46
67
45
62
44
47
12
11
62
9
—
21
7
28
63
54
61
20
49
42
42
62
—
37
44
30
20
29
34
65
42
63
41
44
—
33
58
66
33
8
8
33
9
67
18
13
—
72
35
38
37
28
47
29
53
21
51
40
—
CO2
Extent
Hunting
Introduction
Climate
Extinction
Fires
Nutrients
Disease
Pollution
Humans
Copyright # 2007 John Wiley & Sons, Ltd.
Appl. Cognit. Psychol. 22: 559–572 (2008)
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carbon dioxide in the atmosphere. The other was a link from hunting to extinction, so it also
appears to be a widespread belief that change in the amount of hunting that goes on affects
the rate at which species go extinct.
The MSC was found to be an endorsement frequency of 62, with 28 links meeting this
criterion. However, adding nutrients to the network involved a cost of 10 links, 36% of the
links in the network. By this criterion nutrients are not strongly attached to the network.
The next MSC came at an endorsement frequency of 64. Removing the least strongly
attached entity at this criterion, climate, incurred a cost of only six links, so it is likely that
climate is part of the network. Sixty-four was therefore accepted as the cut-off. The
resultant network is shown in Figure 1.
Each link in this network was endorsed by at least 84% of participants, so it is strongly
consensual. Climate and extent are termini, meaning that they do not have effects on
anything else in the network. However, unlike the network obtained by White (1995), this
network is not strongly unidirectional. There is a feedback loop connecting CO2 and
pollution. There is a more elaborate set of feedback relations connecting hunting,
extinction, introduction and disease. Because participants were not asked to judge the
direction of change, the nature of the feedback in these loops is not clear. Experiment 2 was
Figure 1. Causal network, Experiment 1
Copyright # 2007 John Wiley & Sons, Ltd.
Appl. Cognit. Psychol. 22: 559–572 (2008)
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motivated by the desirability of shedding more light on this. There is also an apparent
subdivision of the network into two parts, which may be characterised as animate and
inanimate. The animate subdivision includes hunting, introduction, extinction and disease,
and the inanimate subdivision includes pollution, CO2, climate and extent. Humans and
fires mediate between the two subdivisions.
Discussion
In one respect the causal network obtained in Experiment 1 resembled that found by White
(1995). Humans were judged to be causal origins, exerting a pervasive influence on other
entities in the network, but not affected by any of them. However, whereas White (1995)
found a virtually unidirectional network, the network found in Experiment 1 is
characterised by feedback loops involving several of the entities in an intricate interactive
structure.
Participants in this experiment were not asked to judge the direction of change that
would occur, nor indeed were the changes described in the stimulus materials in any
particular direction. Because of this it is not clear what form of feedback is present in the
network. It is plausible that some loops at least would be negative feedback loops. For
example, for the loop connecting hunting and extinction, it seems likely that participants
would judge that an increase in hunting would result in an increase in extinction rates,
which would then result in a decrease in hunting, which would in turn lead to a decrease in
extinction rates and thence to an increase in hunting. This is a negative feedback loop.
Since extinction is a one-way street it would still imply a progressive decline in the number
of species, but negative feedback would tend to control the rate at which extinction
occurred. But there is no direct evidence on this or any other loop in the network. For this
reason Experiment 2 was designed to assess the judged directional effects of specific
directional changes in the entities.
EXPERIMENT 2
Method
Participants
Forty first year undergraduate students of psychology participated in return for course
credit. One participants was excluded prior to data analysis for omitting some judgements,
so n ¼ 39.
Materials and procedure
The materials were similar in general form to those of Experiment 1, except that two
versions of each entity were included, one describing an increase and one describing a
decrease. For example, for fires, one entity was ‘increase in the frequency and severity of
fires’ [firesþ] and the other was ‘decrease in the frequency and severity of fires’ [fires].
Terms in parentheses are the identification labels. One entity from Experiment 1, climate,
did not readily lend itself to this manipulation because no particular parameter of climate
had been identified in Experiment 1, so it was omitted. The remaining 10 entities, each in
both increase and decrease versions, were included.
Copyright # 2007 John Wiley & Sons, Ltd.
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The questionnaire therefore began with instructions similar to those used in Experiment
1, except that the opening paragraph pointed out that the change indicated at the top of each
page would be either an increase or a decrease. Participants were also informed that there
were three response alternatives for each of the remaining entities: increase, decrease and
no change. They were instructed to underline whichever of these three they thought would
happen as a result of the change described at the top of the page.
There followed 20 pages similar in format to those used in Experiment 1. An increase or
a decrease in one of the entities was described at the top of each page, and under this were
the remaining nine entities, each accompanied by the three response alternatives in block
capitals.
The procedure was as in Experiment 1.
Results and discussion
The full matrix of endorsement frequencies (out of a total of 39 responses in each cell) is
presented in Table 2. These are net frequencies, obtained by subtracting the number of
decrease judgements from the number of increase judgements. Positive numbers indicate
net judged increases and negative numbers indicate net judged decreases. As in Experiment
1, there was high consensus about some links, with 28 links receiving a net score either
between 33 and 39 or between þ33 and þ39. One link, from huntingþ to extinctionþ,
was endorsed by all 39 participants. The equivalent link in Experiment 1, from hunting to
extinction, had been endorsed by 75 out of 76 participants, so it is likely that most if not all
of them would have judged that an increase in hunting would result in an increase in
extinction.
Table 2. Endorsement frequencies, Experiment 2
Effect
Cause
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
CO2þ
Extentþ
Huntingþ
Introductionþ
Extinctionþ
Firesþ
Nutrientsþ
Diseaseþ
Pollutionþ
Humanþ
CO2
Extent
Hunting
Introduction
Extinction
Fires
Nutrients
Disease
Pollution
Human
1
2
3
4
5
6
7
8
9
10
—
6
2
þ19
12
þ30
11
8
þ23
þ34
—
þ5
2
12
þ9
28
þ4
2
21
37
þ8
—
5
þ1
1
36
þ33
11
24
31
9
—
þ8
2
þ2
þ33
36
þ14
þ22
þ28
1
þ27
—
þ34
24
28
þ11
12
12
þ37
3
28
—
11
þ12
þ23
7
þ9
þ7
35
8
þ35
26
—
þ1
33
þ33
28
29
27
0
34
þ31
—
þ8
þ29
33
þ36
þ29
þ26
þ14
20
þ39
þ6
—
þ35
29
þ37
þ27
þ36
7
þ28
36
1
—
34
þ31
33
31
34
þ2
þ26
þ16
0
þ2
—
þ2
þ5
þ15
þ34
1
16
11
þ2
þ1
—
þ2
1
13
33
8
þ22
14
þ5
þ3
16
—
4
25
27
2
27
þ10
0
þ3
þ16
—
þ12
þ22
þ23
þ2
þ10
þ3
þ21
1
6
þ5
—
þ12
þ19
9
7
þ5
2
þ17
þ7
þ10
—
5
15
þ22
22
þ6
1
þ2
þ37
14
þ10
—
þ38
13
þ23
4
þ2
þ2
30
þ16
7
—
36
6
þ10
þ18
þ11
4
17
þ16
20
11
—
2
5
15
3
þ13
þ17
13
þ18
þ15
—
Copyright # 2007 John Wiley & Sons, Ltd.
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The MSC was found to be an endorsement frequency of 33. Adding nutrientsþ to the
network involved a cost of seven links, but this is not out of line with the cost of adding
further entities, so it is likely that all the entities form a single system. This possibility is
strengthened by the high consensus on the link that adds nutrientsþ to the network, which
was endorsed by 85% of participants. The resultant network is shown in Figure 2.
With more entities and more links, it would be expected that this network would be more
complex than that found in Experiment 1. In fact, however, it is a unidirectional network
with no feedback loops. This can be ascertained by starting with any entity identified as an
Figure 2. Causal network, Experiment 2
Copyright # 2007 John Wiley & Sons, Ltd.
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P. A. White
origin and tracing paths out from it. All such paths end at termini with no recursive links
along the way. The longest path in the network has five links:
human ! fires ! extentþ ! introductionþ ! huntingþ ! extinctionþ. Five entities
are origins, those with rectangular enclosures in Figure 2, and seven are termini, those with
elliptical enclosures in Figure 2. Humanþ and human are both origins in Figure 2, and
human was also an origin in Figure 1. In both experiments, therefore, humans were treated
as affecting other entities in the network but not as being affected by them.
Comparison between the two networks is rendered more difficult by the fact that
Figure 2 represents increases and decreases in the same entity separately whereas Figure 1
does not. Ignoring the link in Figure 1 involving climate, because climate was not included
in Experiment 2, there are 17 links between entities in Figure 1. If we ignore the valence
indicators in Figure 2, nine of those links can also be found in Figure 2, some of them with
both valences. For example, there is a link from human to pollution in Figure 1, and in
Figure 2 there is a link from humanþ to pollutionþ and also one from human to
pollution. Of the eight links in Figure 1 with no equivalent in Figure 2, it appears that
reduced consensus about the directionality of the effect may be responsible. For example,
pollutionþ was judged to result in a change in carbon dioxide levels by 33 participants.
This would qualify for inclusion in the network if they had all made the same judgement,
but five of them judged that the effect would be a decrease in carbon dioxide, resulting in a
net change score of þ23, which does not qualify for inclusion in Figure 2.
At first glance the division of the network into animate and inanimate components found
in Experiment 1 does not appear to have been replicated. Considering the inanimate
components in more detail, climate was not included in the materials for Experiment 2 and
nutrients was not included in the causal network in Experiment 1. In both experiments
pollution and carbon dioxide were connected to the rest of the network via humans and fires
only. Also in both experiments fires were connected to the animate part of the network via
extinction and extent. There is therefore evident similarity between the two experiments in
the ways in which the inanimate components were connected to the rest of the network. Of
the inanimate components, only fires has any effect on the animate components. In both
studies, the other inanimate components either cause effects in each other (Experiment 1)
or are only present as termini, affected only by humans (Experiment 2). Therefore, of fires,
climate, pollution and carbon dioxide, only fires has any effect, either direct or indirect, on
any animate component of the network. In this respect, both studies have produced the
same finding.
Figure 2 is more complex than Figure 1, with 20 entities instead of 10 and 28 links
instead of 17. Despite this, separating increases and decreases in entities has resulted in a
more unidirectional network, and indeed in the complete absence of recursive structures. It
is noteworthy that the minimum endorsement frequency for the links included in the
network was no lower in Experiment 2 (85%) than in Experiment 1 (84%). Thus, the lack of
recursive structures cannot be attributed to lower levels of consensus about the causal links.
GENERAL DISCUSSION
The aim of this research was to take a set of entities identified by experts as causally
important in the ecology of northern temperate forests, and to investigate how these entities
are understood by non-experts as causally connected to each other. Participants were asked
to indicate whether or not there was a causal link between each pair of entities and, in
Copyright # 2007 John Wiley & Sons, Ltd.
Appl. Cognit. Psychol. 22: 559–572 (2008)
DOI: 10.1002/acp
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Experiment 2, to make these judgements separately for increases and decreases in the
entities. Both studies yielded causal networks characterised by a high degree of consensus,
with every link endorsed by at least 84% of participants. However, the two studies yielded
causal networks with different structural properties. The network in Experiment 1 was
characterised by feedback loops, particularly by a complex interactive structure involving
hunting, extinction of species, introduction of new species and diseases. When increases
and decreases were subject to separate judgements in Experiment 2, the feedback loops
disappeared and the resultant network was unidirectional. Human, nutrients and increase in
diseases formed the causal origins in the network. Carbon dioxide, pollution, extinction
and a decrease in introductions of new species formed the termini of the network, affected
by other entities but not affecting any of them.
Figure 2 has a claim to be a more valid representation of the consensual causal network than
Figure 1 does for two reasons: it unconfounds increases and decreases, and it also
unconfounds different individual beliefs about the effects of given changes. For example, in
Figure 1 there appears to be a two-way connection between hunting and extinction. In
Figure 2, however, there are two links from hunting to extinction but none from extinction to
hunting. What has changed? Unconfounding increases and decreases has revealed individual
differences, showing insufficiently high consensus about the effects of an increase in
extinction. There were 29 participants who judged that an increase in extinction would result
in a decrease in hunting, but also 5 who judged that the opposite would occur. Because of this,
the consensus about the direction of effect of an increase in extinction was too low for this link
to make it into the consensual network. Thus, the apparent feedback loops in Figure 1 are not
consensual, because they confound individually different beliefs about unidirectional effects
of certain changes. Thus, when the confounds are removed by asking for separate judgements
about increases and decreases, a true consensual network appears and the apparent interactive
loops in Figure 1 resolve into separate unidirectional links. This is evidence that people
generally understand ecosystems as operating with unidirectional causality.
It could be argued that these findings are specific to the entities chosen for inclusion in
the study, and that the inclusion of other entities might result in significant differences in
causal judgement. This is undoubtedly a possibility. Against this, however, the
unidirectional character of the network is formally similar to that obtained by White
(1995) with a different set of entities, and this indicates that a tendency for causal beliefs to
fall into unidirectional networks is independent of the particular entities selected for
inclusion. Also, the structures obtained in this study are meaningful because the entities
included were those identified by experts as most causally important in temperate forest
ecology. There is a case for studying beliefs about other kinds of ecosystems where
different factors might be important, however.
The exact contents of the causal network obviously depend on endorsement frequencies.
Two relevant observations can be made about this. One is that each link in the network in both
studies was endorsed by at least 84% of participants. This means that some links were not
included even though they were endorsed by about three-quarters of participants. There
could therefore be relatively high consensus about links that do not meet the MSC, and
adding these to the network could significantly change its formal properties. The second
observation is that small changes in endorsement frequencies around the cut-off point could
also make a significant difference to the content of the network, and therefore possibly also to
its structure. If a link with an endorsement frequency of 32 in Experiment 2 had been
endorsed by just one more participant it would have met the MSC and would have been
included in the network. In fact, as Table 2 shows, no link was endorsed by 32 participants,
Copyright # 2007 John Wiley & Sons, Ltd.
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which adds to the evidence that the MSC has validly distinguished those links that form a
legitimate part of the consensual causal structure from those that do not. However, four links
were endorsed by 31 participants, which perhaps is enough cause for concern.
The MSC is an objective rule for selecting links to include in the network, and it is not
good practice to make gratuitous exceptions to the rule simply because links that did not
meet the criterion were frequently endorsed. However, it is legitimate to ask what
endorsement frequency would be needed in Experiment 2 to produce a feedback loop in the
causal network. It turns out to be 22. At a frequency of 23 the link from pollutionþ to
carbon dioxideþ enters the network, and at a frequency of 22 the link from carbon
dioxideþ to pollutionþ enters. These two links form a positive feedback loop with each
other, but no other links are involved in the interaction. A frequency of 22 is 11 below the
MSC and represents a consensus of just over half of the participants. At this level of
consensus there is no guarantee that the participants who endorsed one link are the same as
those who endorsed the other one. It is therefore safe to conclude that the network found in
Experiment 2 is robustly unidirectional.
One possible concern about the results is that the method of elicitation may result in an
inaccurate representation of the consensual network of beliefs that exists in people’s minds.
The method used here requires individuals to consider each of a large number of pairs of
links, a specific focus in which they may lose sight of the overall structure of their beliefs.
This can be compared with a different method of elicitation, the diagram method (Green &
McManus, 1995; Green, Muncer, Heffernan, & McManus, 2003). In this method all
entities are presented on a single sheet of paper and participants draw lines with arrows to
denote causal links. This method might encourage an approach focussed more on the
overall structure of the network, which is visible to the participant as they proceed with the
line-drawing task.
Green et al. (2003) compared the present method, which they called the grid method, and
the diagram method, in a study of causal beliefs about loneliness, and found that the networks
produced by the two methods were very similar. Factor analysis also revealed evidence that
both methods yielded a consensual representation. This evidence of consensus was obtained
despite the fact that endorsement rates were quite low with both methods. In the diagram
method only one link was endorsed by more than 50% of participants. In the grid method
participants judged each link on a 5-point Likert scale, and only 28% of the sample rated all
links included in the network at two (the second lowest rating) or more. This is much lower
than the levels of consensus reported above for the present experiments. The study by Green
et al. therefore gives reason for thinking that the network obtained in this study is both valid
and a consensual representation of people’s beliefs.
A related concern is that the grid method might reduce the likelihood of obtaining
feedback loops. However, in the study by Green et al. (2003), the diagram method yielded a
network with no feedback loops, so there is no evidence that a graphical method improves the
likelihood of obtaining feedback loops. Indeed, feedback loops were found in Experiment 1,
and it was only when increase and decrease judgements were separated in Experiment 2 that
the feedback loops disappeared. The presence of feedback loops in Experiment 1 therefore
shows that the grid method does not militate against such phenomena. It is only necessary to
be certain that the feedback loops that appear are genuine consensual representations.
The results do not mean that individuals are incapable of formulating interactive models
of ecological systems. Green (2001) showed that a majority of participants produced
interactive accounts involving two and sometimes three entities (plant, herbivore,
carnivore) in an ecological microworld when asked to explain a complex pattern of
Copyright # 2007 John Wiley & Sons, Ltd.
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571
fluctuation over time in the population of the herbivore. However, this system comprised
only three entities and individuals were constrained to explain a complex pattern presented
to them, rather than envisaging themselves what sort of pattern might occur. It is therefore
not clear whether the interactive thinking exhibited in this study is characteristic of
reasoning about ecological systems outside the psychological laboratory. The studies by
Green (1997, 2001) do suggest that people have a capacity to think about interactions in
natural systems, but that this capacity is overwhelmed by task complexity. But natural
ecological systems are complex (e.g. Yodzis, 2000), so the way people think about such
systems is very likely to be not just oversimplified but linear and unidirectional, resulting in
a profound misconception of the causal interactions that occur in natural ecosystems.
This research has focussed on the content and structure of causal beliefs and not on the role
they play in cognition and social interaction. One possibility is that the network of beliefs
may be modelled as a set of associative bonds between the entities. Exactly what sort of
model might best describe such a network lies outside the scope of the present enquiry;
however, if it is assumed that the distance between entities in the causal network is an
approximate index of the strength of the associative bond between them, then some simple
predictions for cognition and social interaction can be generated. Thus, when one is told
about a series of major bush fires in Australia, for example, the cognitions most likely to be
elicited are those concerning entities most directly connected to increase in fires in the
network because those are the ones with the strongest associative bonds to increase in fires.
Therefore one might worry about endangered species going extinct or a possible rise in
pollution levels, but one would be less likely to think about an increase in disease in the forest
ecology or a decrease in nutrients, because these entities have less strong bonds with an
increase in fires. Additionally, Green et al. (2003) pointed out that mental representations of
systems contribute to communicative exchanges between individuals, which would tend to
enhance the consensual nature of causal beliefs. Thus, although causal networks are
undoubtedly subject to variation between individuals, social processes involving the sharing
of information would act to minimise that variation. There is clearly a need for more research
on the implications of causal networks for cognitive activity and social interaction.
The unidirectionality of the causal network obtained in Experiment 2 is consistent not
only with the causal network obtained by White (1995) but also with other studies showing
that causality in natural systems is construed as unidirectional (White, 1992, 2000). When
tracing the effect of a change or perturbation through the structure of a complex physical
system, people tend to judge that the effect will travel in one direction only, away from the
locus of the perturbation in terms of the structure of the system and they do not appear to
appreciate or predict interactive effects. The complexities of interactive systems with
multiple entities are admittedly hard to grasp, but a failure to appreciate even the possibility
of interactions and feedback loops in systems with more than three entities seriously
compromises lay understanding of the workings of the natural environment, with
potentially detrimental consequences for the global ecosystem.
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