In stock-flow relationships inflows and outflows of a stock accumulate over time. Previous studies have shown people’s poor understanding of accumulation. This study examines the relationship between performance in a stock-flow experiment and depth of information processing. Half of the participants read a text about the dangers of climate change to prompt deeper processing (and understanding) of the following stock-flow task. Then they were given one of two scenarios in which the atmospheric carbon dioxide level rose/fell from year 2000 and stabilized at a higher/lower level until the year 2100. They had to choose the correct carbon dioxide emissions and uptake trajectory in order to realize the development of the carbon dioxide level in their scenario. Performance was low. More than half of the participants chose emission trajecto-ries similar to the shape of the atmospheric carbon dioxide curve which is an indication for pattern matching. Giving participants information about the dangers of climate change did not increase their performance.
Table of Contents
1 Introduction
2 Theoretical background
2.1 The carbon cycle
2.2 The climate system
2.3 Climate change
2.4 Public beliefs about climate change
2.5 Stock-flow processes
2.6 Aim of this study and hypotheses
3 Method
3.1 Participants
3.2 Task description
4 Results
4.1 Preliminary analyses
4.2 Results regarding demographic variables
4.2.1 Ordinal data
4.2.2 Binary data
4.3 Results regarding the hypotheses
4.4 Qualitative analysis
5 Discussion
6 Conclusion
Research Objectives and Themes
This thesis examines the relationship between performance in a stock-flow experiment and the depth of information processing regarding climate change. The primary research question is whether emphasizing the dangers of climate change enhances the depth of information processing and, consequently, improves participant performance in managing complex climate-related stock-flow structures.
- Stock-flow relationships and human reasoning errors
- Impact of information framing on cognitive processing
- Mental models of climate change and accumulation processes
- Heuristic vs. systematic information processing in decision-making
Excerpt from the Book
2.5 Stock-flow processes
Several studies show that people do not have any intuitive feeling of stocks and flows – the process of accumulation (Cronin, Gonzalez & Sterman, 2009; Dutt & Gonzalez, 2009; Dutt & Gonzalez, 2013; Moxnes & Saysel, 2009; Newell, Kary, Moore & Gonzalez, 2015; Sterman, 2008; Sterman & Booth Sweeny, 2007). Even highly educated laypeople have difficulties solving stock flow tasks correctly. In stock flow relationships there is one stock, one inflow to the stock and one outflow from the stock. So the level of the stock at a certain point of time is the previous level plus the difference between inflow and outflow. The stock falls when the inflow is less than the outflow and rises when the inflow is exceeding the outflow. It remains stable when inflow and outflow are equal. Instead of taking this relationship into account people often rely on a pattern matching heuristic where outputs are correlated with inputs (Cronin et al., 2009; Dutt & Gonzalez, 2009; Dutt & Gonzalez, 2013; Moxnes & Saysel, 2009; Newell et al., 2015; Sterman, 2008; Sterman & Booth Sweeny, 2007). They assume that the accumulating variable (the stock) should “look” like the inflow while disregarding the outflow (pattern matching). This allows for quick problem solving in simple linear systems, but fails at more complex ones. With stock flow structures the stock can continue to rise even if the inflow falls as long as the inflow exceeds the outflow. Pattern matching would falsely suggest that the stock must fall, because the inflow is falling. Chen (2011) explains the robust nature of pattern matching with static mental models that ignore change over time. Stock-flow relationships are dynamic in that the difference between inflow and outflow defines not the level of the stock but its change at a particular point of time.
Summary of Chapters
1 Introduction: Provides an overview of climate change politics, the necessity of public understanding, and the role of mental models in decision-making.
2 Theoretical background: Establishes the scientific basis of the carbon cycle, climate systems, and explains the cognitive challenges inherent in stock-flow reasoning.
3 Method: Details the experimental survey design, participant demographics, and the manipulation of information framing.
4 Results: Presents the statistical findings regarding participant performance, demographic influence, and qualitative insights into decision-making logic.
5 Discussion: Interprets the findings in the context of cognitive psychology and human inability to grasp complex accumulating systems.
6 Conclusion: Summarizes the implications of these findings for climate communication and the urgent need for better methods to bridge the gap between scientific knowledge and public action.
Keywords
Climate change, accumulation, mental models, global warming, information processing, correlation heuristic, stock-flow reasoning, cognitive bias, decision-making, systematic processing, heuristic processing, pattern matching.
Frequently Asked Questions
What is the core focus of this thesis?
The thesis focuses on how individuals understand the accumulation processes (stocks and flows) within the context of the climate system and why they often struggle with these concepts.
What are the primary thematic areas covered?
The study covers climate change science, cognitive psychology related to stock-flow reasoning, public perception of environmental risks, and information processing models.
What is the main research objective?
The goal is to determine if priming participants with information about the dangers of climate change increases the depth of their information processing, thereby improving their ability to solve stock-flow tasks.
Which methodology was employed for the study?
A quantitative online survey experiment was conducted where 116 German-speaking participants were divided into groups and tasked with estimating emission and removal rates to stabilize CO2 levels.
What topics are discussed in the main body of the paper?
The main body reviews the carbon cycle, the climate system, human cognitive limitations in understanding feedback loops, and experimental results evaluating participant decision-making.
Which keywords best characterize the research?
Key terms include climate change, mental models, information processing, accumulation, and pattern matching.
How does "pattern matching" hinder correct climate decision-making?
Participants incorrectly assume that the output (stock level) should always move in the same direction as the input (emissions), leading them to ignore the influence of outflow (natural removal) on the system.
Did the manipulation of "dangerous" information have the expected effect?
No, the hypothesis was rejected; participants who read the text highlighting the dangers of climate change did not perform significantly better than those who did not.
- Quote paper
- Marie-Rose Degg (Author), 2017, Depth of information processing as a moderator to mental models of climate change, Munich, GRIN Verlag, https://www.grin.com/document/448864
