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Summary on behaviour
Topic: Behaviour
by Will, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
The Problem#
When something changes in a system, the underlying cause is often unclear. Complex systems, such as trade, climate, and politics, are driven by innumerable factors. The relationships between these factors are often unclear. So how can we begin to explore changes in system behaviour, and how do we investigate what causes these changes?
A Solution#
Richardson suggests that behaviour over time (BOT) graphs are a plausible solution to this problem.1 BOT graphs demonstrate how behaviours change over time. Correlations between these changing behaviours then allow researchers to hypothesise what causes these changes. 2
Richardson invites us to consider the Iroquois, a nation of indigenous Americans, and their shift from buffalo hunters to farmers. This can be explored with the BOT graphs below.
1 Richardson, G. (1998). Getting Started with Behavior Over Time Graphs: Four Curriculum Examples. The Creative Learning Exchange.
2 The Systems Thinker. (2018). Behavior Over Time Diagrams: Seeing Dynamic Interrelationships- The Systems Thinker. [online] Available at: https://thesystemsthinker.com/behavior-over-time-diagrams-seeing-dynamic- interrelationships/ [Accessed 23 Mar.
We can then overlay these two relationships to compare them.
From this we know that there is a general increase over time in Iroquois farming practices. We also know that the rate of this increase is greatest when buffalo numbers are at their lowest. Finally, we can assume that hunting leads to a reduction in buffalo supply. These correlations allow us to hypothesise as follows:
Initially the Iroquois were predominantly hunters. As they hunted, buffalo populations decreased leading them to either move on or place a greater emphasis upon farming. As they farmed, they became more sophisticated farmers, farming at an exponentially increasing rate. We can understand this process through a systems diagram.
BOT graphs allow us to compare relationships between changing behaviours, observe correlations, and hypothesise their causes. In this case they were useful in understanding why the Iroquois moved from being hunters to farmers. They are thus for solving complex problems.
Shortcomings#
Yet there are three shortcomings to BOT graphs.
First, BOT graphs can only show correlation, not causation. This is problematic because a correlation might lead us to assume a causation exists where it does not. In the above BOT graph the changes in buffalo populations might have been a function of seasonal migration, not hunting. This means that our inference that Iroquois hunting caused changes in buffalo populations is potentially misleading. The uncertainty between correlation and causation undermines the usefulness of BOT graphs.
Second, BOT graphs potentially lead researchers to suffer from independent variable bias. It is impossible to consider every fact in a complex system. To account for this, researchers only consider those variables they already consider having an important impact on the system in question. Indeed, because BOT graphs are based on data, they can only be drawn for those factors there is data for. Data is only gathered for factors that are considered significant. Thus, researchers may accidentally ignore variables which are important. BOT graphs are consequently limited in their explanatory value.
Last, complex systems are constantly changing. Sometimes events are so significant that they completely alter the function of a system. An example of this might be the introduction of smartphones and their effect on the way people communicate. BOT graphs cant consider unforeseen black swan events such as these because they are derived from retrospective data. This undermines the predictive value of BOT graph-based analysis.
Conclusion#
Behaviour Over Time graphs can only be a starting point, a tool to use to aid in channelling research efforts. Care must be taken not to be misled by convenient correlations.
Explore this topic further#
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Behaviour in the Primer
Disclaimer#
This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
The Problem#
When something changes in a system, the underlying cause is often unclear. Complex systems, such as trade, climate, and politics, are driven by innumerable factors. The relationships between these factors are often unclear. So how can we begin to explore changes in system behaviour, and how do we investigate what causes these changes?
A Solution#
Richardson suggests that behaviour over time (BOT) graphs are a plausible solution to this problem.1 BOT graphs demonstrate how behaviours change over time. Correlations between these changing behaviours then allow researchers to hypothesise what causes these changes. 2
Richardson invites us to consider the Iroquois, a nation of indigenous Americans, and their shift from buffalo hunters to farmers. This can be explored with the BOT graphs below.
1 Richardson, G. (1998). Getting Started with Behavior Over Time Graphs: Four Curriculum Examples. The Creative Learning Exchange.
2 The Systems Thinker. (2018). Behavior Over Time Diagrams: Seeing Dynamic Interrelationships- The Systems Thinker. [online] Available at: https://thesystemsthinker.com/behavior-over-time-diagrams-seeing-dynamic- interrelationships/ [Accessed 23 Mar.
We can then overlay these two relationships to compare them.
From this we know that there is a general increase over time in Iroquois farming practices. We also know that the rate of this increase is greatest when buffalo numbers are at their lowest. Finally, we can assume that hunting leads to a reduction in buffalo supply. These correlations allow us to hypothesise as follows:
Initially the Iroquois were predominantly hunters. As they hunted, buffalo populations decreased leading them to either move on or place a greater emphasis upon farming. As they farmed, they became more sophisticated farmers, farming at an exponentially increasing rate. We can understand this process through a systems diagram.
BOT graphs allow us to compare relationships between changing behaviours, observe correlations, and hypothesise their causes. In this case they were useful in understanding why the Iroquois moved from being hunters to farmers. They are thus for solving complex problems.
Shortcomings#
Yet there are three shortcomings to BOT graphs.
First, BOT graphs can only show correlation, not causation. This is problematic because a correlation might lead us to assume a causation exists where it does not. In the above BOT graph the changes in buffalo populations might have been a function of seasonal migration, not hunting. This means that our inference that Iroquois hunting caused changes in buffalo populations is potentially misleading. The uncertainty between correlation and causation undermines the usefulness of BOT graphs.
Second, BOT graphs potentially lead researchers to suffer from independent variable bias. It is impossible to consider every fact in a complex system. To account for this, researchers only consider those variables they already consider having an important impact on the system in question. Indeed, because BOT graphs are based on data, they can only be drawn for those factors there is data for. Data is only gathered for factors that are considered significant. Thus, researchers may accidentally ignore variables which are important. BOT graphs are consequently limited in their explanatory value.
Last, complex systems are constantly changing. Sometimes events are so significant that they completely alter the function of a system. An example of this might be the introduction of smartphones and their effect on the way people communicate. BOT graphs cant consider unforeseen black swan events such as these because they are derived from retrospective data. This undermines the predictive value of BOT graph-based analysis.
Conclusion#
Behaviour Over Time graphs can only be a starting point, a tool to use to aid in channelling research efforts. Care must be taken not to be misled by convenient correlations.