Resilience is a multifaceted concept that is defined rather loosely. The word “resilience” originates from Latin language, dating from the early 17th century. It is composed of “re-” meaning “back” and “salire” meaning to jump, to move up or to leap. Most definitions of resilience relate to bouncing back. At the same time, the concept of resilience varies across domains. Some literature describes resilience as a property, such as the preparedness and the ability to recover from a shock or distress. Other sources relate to the actual behavior of a system or a person when experiencing a disruption. Some definitions highlight the importance of rapidity of the reaction to the shock and fast recovery. Further definitions state that the recovery should bring the performance back to the pre-shock level, while other literature indicates that resilience also implies excelling beyond the initial level.

The focus of different aspects of resilience depends on the scientific domain it is used in. For example, individual resilience focuses on a person’s ability to cope with misfortune. It is related to one’s psychological, physical, financial and social wellbeing. In contrast, in many engineering domains, resilience focuses on loss reduction and maintaining the infrastructure functionality. Resilience can relate to a person, or to an infrastructure, a digital, a financial, or a social system. Depending on the area of application, different aspects of resilience may be of various importance. However, the currently available literature offers little classification of these definitions.

In this project, we have reached out to a number of resilience experts in academia, policymaking and business, to better define resilience in general, and to clarify differences in resilience across various domains such as infrastructure, finance and social networks. We found that, despite having the same root, definitions and meaning of resilience differs across domains. The figure below demonstrates that "recovery" is the only keyword that best describes four types of resilience: infrastructure resilience, psychological, social and financial resilience. This example poses a question of whether resilience is a science or only a "buzz word". You can find more findings about the meaning fo resilience in [1].

Source: Andraszewicz, S., Roberts, A.C., Wettstein, L. & Straub, L.M. (2024). What is resilience? An aggregated expert opinion. https://doi.org/10.31219/osf.io/r97t3

What does decision-making in urban spaces and built environments have in common with decision-making during financial planning and crises? In both cases, decision-making is inherently complex. It requires dealing with uncertainty, it consumes a substantial amount of cognitive resources and it may have no single best solution.  Complexity of decision-making is a multifaceted concept that appears in a wide range of scientific and application domains, but it lacks a uniform definition. This poses a problem when dealing with complex decisions and when trying to find ways to simplify them. Therefore, based on a scoping review, I  outline a conceptual framework that systematises knowledge about complex decision-making and organises it into a conceptual framework (see the Figure below). 

Source: Andraszewicz, S. (2023). A conceptual framework of complexity in decision-making. https://doi.org/10.31219/osf.io/u6fzv

The Zurich Trading Simulator (ZTS) software is a free app for oTree, designed to study risk-taking and trading behaviour in a dynamically evolving environment. Unlike other experimental tasks, ZTS provides participants with a real-time experience of asset price movements in a continuous trading setting, closely resembling real-world financial markets. This dynamic framework enables the measurement of trading activity, including trade volume, frequency, and the value of traded assets. Additionally, ZTS assesses risk-taking by analysing the proportion of risky assets in a participant’s portfolio. As an open-source tool, ZTS allows researchers to customise its standard setup to suit their experimental needs. We used this tool to conduct a series of experiments, in which we simulated social trading platforms, we induced overconfidence and we measured anticipatory arousal during trading.

Source: Andraszewicz, S., Friedman, J., Kaszás, D., & Hölscher, C. (2023). Zurich Trading Simulator (ZTS)—A dynamic trading experimental tool for oTree. Journal of Behavioral and Experimental Finance, 37, 100762. https://doi.org/10.1016/j.jbef.2022.100762

In financial markets, profit is usually associated with risk-taking, as those who take risks, use the opportunities that markets present. However, during market bubbles, risk-taking might lead to losses, whereas risk aversion can lead to more profit. Emotion-based warning signals might play a role here by helping to recognize when risk aversion is preferable. To study this, we used a trading simulator, where 27 male participants traded on a historical stock price trend during a market bubble-and-crash scenario, and we continuously monitored their skin conductance level. We found that participants earning the most were characterized by an adaptive pattern of risk-taking —they invested much in the asset in the initial phase of the bubble but sold their stocks before the crash. Their skin conductance level was closely associated with the price trend, peaking before the crash started. This suggests that skin conductance provided a bodily warning signal in this group. Moreover, in high earners, skin conductance level correlated negatively with the proportion of stocks, indicating that the high earners used this warning signal to sell stocks. These results underscore the adaptive role of bodily signals in decision-making and elucidate the neural basis of success in uncertain financial markets.

Source:  Wichary, S., Allenbach, M., von Helversen, B., Kaszás, D., Sterna, R., Hoelscher, C., & Andraszewicz, S. (2023). Skin conductance predicts earnings in a market bubble-and-crash scenario. https://doi.org/10.31219/osf.io/ybu8z 

Autonomous vehicles are an exciting technology that could revolutionise the way we commute. Moving around busy streets with pedestrians, cyclists and human drivers poses a challenge to self-driving cars. Past accidents involving first versions of autonomous cars raised concerns from human traffic participants. Virtual Reality - an immersive technology that enables simulating various hypothetical situations in a form of computerised games - can help pre-test these situations before they are introducted in the real life. This approach has been used in architecture to test the functionality of buildings before they  are built. We tested whether VR can prove useful for investing human interaction with autonomous vehicles. We created a street crossing task, in which we  presented people a movie of a car approacing with a constant speed of 30 miles/h. We asked the participants to indicate the last safe moment to cross the street. Some of the cars were labelled as autonomous, some as driven by a human. We presented a movie of a "neutral-looking" black passenger car with VR and a movie or a real car driving on a street. The angle of the view and the car speed were the same in all cases. We found no differences in the response time between autonomous and human-driven cars presented in the VR movies. Most of the respondents would enter the street too late, potentially causing an accident, despite the fact that their theoretical knowledge about the safe distance to cross the street in front of an approaching car was correct. In contrast, our respondents were much more cautious when they observed a movie of a real car. The potential explanation is that VR does not present the spacial dimensions in a sufficiently realistic fashion to let people judge the safe distance from and speed of a moving object. This study is an example of the limitations of using immersive technologies for pre-testing technologies that require high level of safety.

Blockchain technologies are products and services based on digital tokes whose complete history can be tracked. They are also known for their lack of regulation and democratisation. Several countries are already testing the use of blockchain for introducing digital versions of their currencies. However, in blockchain, we can identify stable coints and altcoins. Stable coins are directly linked to fiat currencies like Euro, US dollar or Swiss frank. Their value does not fluctuate with respect to the fiat currencies. In contrast, altcoins are tokens whose value is not fixed with any fiat currency which makes them highly volatile financial asset class. Together with a cryptotrading platform in Europe, we conducted a study on the users of cryptocurrencies. We tested an extended technology adoption model that included mindset (see the figure). People with the fixed mindset tend to believe that their skills and properties are innate and cannot be altered. In contrast, people with the growth mindset believe that their skills can be improved.  We found that mindset plays a role in the intention to use altcoins, but not to use stable coins. We also found that users of cryptocurrencies are predominantly male, good-earners with high financial and blockchain knowledge. This contradicts a common belief that investors in crypto assets are naive investors without previous knowledge. However, our sample consisted of users of a specialised platform for trading blockchain tokens, which could induce a self-selection bias. We also conducted a study with about 200 cryptocurrency users and 200 non-users, sampled from a general population.  This project is still work in progress and more insights from this study are about to come!

European (MiFiD) and US regulation requires financial institutions offering investment products such as stock, bonds or ETFs (Exchange Traded Funds) to assess their customers' ability to bear losses their individual propensity to take risk. Unfortunately, this is "easier said than done". Despite a wide variety of methods assessing a person's risk appetite, these methods may provide diverging and inconsistent results. For example, one measure may assess a person as being risk-averse, while another measure would assess the same person as risk-seeking. Another problem is that the same measure may result in a different result when applied to the same person at different points in time, for example now and in three months in time from now. Self-reported measures, which are measures that directly ask people to assess people's own propensity to take risk tend to have a higher test-retest reliability, meaning that they provide a more consistent results over time. In contrast, behavioural measures, which are measures that "observe" a person's behaviour to assess their risk appetite, provide a quantitative assessment of a person's risk propensity, but this assessment is less consistent over time.

One idea to address these problems is to link risk-taking with individual traits, such as intelligence or personality.  To test this, we conducted a meta-analysis - a statistical method that compares the statistical effects across all available studies that tackle the problem of interest. Our meta-analysis considered two types of behavioural risk propensity measures and a simple intelligence test called Cognitive Reflection Task (CRT). The CRT is a task composed of three mathematical problems that do not require higher mathematical skills but require suppressing one's immediate response that may suggest an incorrect answer. Our study (see [1] for more details) shows that individuals with higher cognitive skills are NOT more risk-taking, but they can better understand the task measuring their risk appetite. Therefore, responses of people with higher cognitive skills may be less influenced by the task that should elicit their risk appetite. Therefore, in [2], we investigated whether implementing several simplifications could improve elicitation of people's risk appetite. We created a task which simplified the presentation of a numeric choice with using simple probability structure of a 50/50 choice. We created a cover story describing the choice problem and we used graphical user interface features presenting multiple choice options. Also, we included choices offering both gains and losses. In finance, risk is conceived as a potential variability of outcomes, but it could correspond to the variability within gains. However, previous literature shows that "lay people" see risk as a potential loss rather than as variance. These simple additions improved consistency in measurement, which scientists call a test-retest reliability. This is a stepping stone in finding the best solution to successfully evaluate one of the crucial aspects of our behaviour - risk taking.

Publications:

1. Mechera-Ostrovsky, T., Heinke, S., Andraszewicz, S. & Rieskamp, J. (2022). Cognitive abilities affect decision errors but not risk preferences: A meta-analysis. Psychonomic Bulletin & Review, 29(5), 1719-1750, https://doi.org/10.3758/s13423-021-02053-1

2. Heinke. S., Schürmann, O., Andraszewicz, S. & Rieskamp, J. (2024). Improving behavioral risk-preference measures: Many decisions with gains and losses increase test-retest reliability. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4885566

Crises occur cyclically and can lead to significant financial, social, psychological, political, infrastructural, or health-related shocks. They are characterised by the perceived value of loss, probability of loss, and associated stress, creating complex decision problems that many leaders must navigate. In an article and a book chapter, we discuss a framework of complexity in decision-making to help identify why certain decisions are particularly challenging and what strategies can be employed to address them effectively. We outline well-established techniques from behavioural sciences for analyzing and quantifying decision problems and propose a three-step action plan to reduce decision complexity. The goal of this article is to translate scientific insights into practical applications, recognising that while human nature remains relatively stable, the evolving nature of crises requires adaptive decision-making approaches.

Sources:

1. Andraszewicz, S. & Hölscher, C. (2023). Chapter 5: Decision-making in complexity of crisis, in Crisis Leadership: A guide for leaders, (eds) Khader, M., Tan, E., Toh. B., Siew-Maan, D., Chua, S., World Scientific, pp. 77-90, https://doi.org/10.1142/9789811262456_0005

2. Andraszewicz, S. & Hölscher, C. (2024). Dealing with complexity of decision-making in a crisis. Home Team Journal, 13, 35-46, https://www.mha.gov.sg/hta/publications/publications-content/publications/home-team-journal-no.-13 

Financial bubbles and crashes have been fascinating academics and finance professionals. They can have a big impact on the economy and they are driven by the psychology of many interacting individuals. Since 1980s, behavioural scientists have been using computerised tasks to create artificial financial markets to investigate factors that drive excessive stock prices. We created a market that mimicked the trading rules of the Swiss Stock Exchange market (SIX) to allow over a hundred of semi-experienced student investors to trade multiple financial assets (up to 40) over a period of four weeks. They could place orders at any time and from any place as long as they had internet access. Before we opened the market, we first elicited the investors' belief about the future value of these assets. The value of the asset would depend on an actual real-life event. After the market closed, we again elicited participants' belief about the value of the assets. We documented that the market prices mapped the averaged belief of all market participants. The pre- and post-trading belief distribution did not differ much and they did not differ from the asset prices. The price distributions substantially shifted when we introduced "news", which was vague and uncertain information distributed to only half of the market participants. We also found that market participants have a tendency for having a bell-shaped belief, with the middle values being most likely, and tail values receiving lower probabilities. This tendency was surprising in markets, in which historical data would lead to a uniform distribution of prices. In a uniform distribution, all values are equally likely. We also asked participants to provide their reason for putting an order. About half of the orders were motivated by the traders' "gut feeling". 

Source:

1. Sornette, D., Andraszewicz, S., Wu, K., Murphy, R.O., Rindler, P. & Sanadgol, D. (2020). Overpricing persistence in experimental asset markets with intrinsic uncertainty. Economics: The Open-Access, Open-Assessment E-Journal, 14(20), 1-53, https://doi.org/10.5018/economics-ejournal.ja.2020-20 

Sornette, Andraszewicz et al. (2020) conducted a naturalistic experiment examining mispricing and coordination of market players in a realistic simulated stock market. This experiment lasted a couple of weeks, while participants could access the trading platform from any place as long as they had access to the internet. Also, they could directly experience the events and their uncertainty relating to the asset prices that they were trading. We wanted to test whether these features plays an important role in complex tasks such as trading. With the aim to investigate how well a complex real-life task can be translated to laboratory settings, we conducted a replication study. We replicated the study described in Sornette, Andraszewicz et al. (2020) in laboratory conditions by adjusting the experiment duration and instructions. As in the naturalistic experiment, we found a convergence between the belief and price distributions. Also, our market resulted in market bubbles. However, the dynamics of laboratory market substantially differed from the naturalistic setting. The trading volume was much lower, a relatively large portion of the trading time was needed to coordinate the initial prices and there were many "penny" stocks compared to the naturalistic market. Therefore, the Scientific investigation of complex tasks may require timeframes and environments that mimic these tasks in the real world.

Source: Andraszewicz, S., Wu, K. & Sornette, D. (2020). Behavioural effects and market dynamics in field and laboratory experimental asset markets. Entropy, 22(10), 1183, https://doi.org/10.3390/e22101183

We experimentally simulated a social trading platform - an online service for investing in financial assets that allows for connecting and following other users. Social trading platforms  integrate the features of a trading app and social media. Often, they put the best performing users in the spotlight. To other users, this may have a similar psychological effect to watching celebrities on social media. In our experiment, comparing oneself to the unreachable top performers resulted in investing more in stocks and doing more high-volume transactions. This increased effort did not result in higher earnings, but it decreased investors' satisfaction from their trading activity. Investing has become easy and accessible for everyone with access to the Internet and minimal financial resources. This opens many opportunities but potential risks such as enhanced social influence need to be considered. This aspect should be especially relevant to financial regulators.

Source: Andraszewicz, S., Kaszás, D., Zeisberger, S. & Hölscher (2023). The influence of upward social comparison on retail trading behaviour. Scientific Reports, 13, 22713, https://doi.org/10.1038/s41598-023-49648-3

Social comparison may be one of the drivers leading to depletion of common resources, such as clean air, drinking water, or public infrastructure, just to name a few. We conducted a simple experiment in which participants were asked to take the role of fishermen and take fish from the lake. They could take small or large net sizes of fish and for every fish, they would receive monetary compensation. However, the resource was depleting and the fish could reproduce at a fixed rate. Half of the participants were informed that the resource was depleting because the fish migrated to a different lake. The other half of the participants was informed that the resource was depleting because two other fishermen also take fish from the lake. As depicted in the figure, participants exposed to social comparison to two other fishermen acted competitively, such that they would take as much or more fish than the two other fishermen. At the same time, participants without the social comparison would try to maximise their earnings while not exceeding the fish outflow to another lake. This difference in competitive vs. preserving behaviours of the two groups was linked to how their brain responded to the social comparison. Ventral Striatum - a small brain structure located under the cortex moderated the behaviour resulting from the presence or lack of social comparison. 

Source: Martinez-Saito, M., Andraszewicz, S., Klucharev, V. & Rieskamp, J. (2022). Mine or ours? Neural basis of the exploitation of common-pool resources. Social Cognitive and Affective Neuroscience, 19(9), 837-849, https://doi.org/10.1093/scan/nsac008

When making risky decisions, people should evaluate the consequences and the chances of the outcome occurring. We examine the risk-preference hypothesis, which states that people's cognitive abilities affect their evaluation of choice options and consequently their risk-taking behavior. We compared the risk-preference hypothesis against a parsimonious error hypothesis, which states that lower cognitive abilities increase decision errors. Increased decision errors can be misinterpreted as more risk-seeking behavior because in most risk-taking tasks, random choice behavior is often misclassified as risk-seeking behavior. We tested these two competing hypotheses against each other with a systematic literature review and a Bayesian meta-analysis summarizing the empirical correlations. Results based on 30 studies and 62 effect sizes revealed no credible association between cognitive abilities and risk aversion. Apparent correlations between cognitive abilities and risk aversion can be explained by biased risk-preference-elicitation tasks, where more errors are misinterpreted as specific risk preferences. In sum, the reported associations between cognitive abilities and risk preferences are spurious and mediated by a misinterpretation of erroneous choice behavior. This result also has general implications for any research area in which treatment effects, such as decreased cognitive attention or motivation, could increase decision errors and be misinterpreted as specific preference changes.

Source: Mechera-Ostrovsky, T., Heinke, S., Andraszewicz, S. & Rieskamp, J. (2022). Cognitive abilities affect decision errors but not risk preferences: A meta-analysis. Psychonomic Bulletin & Review, 29(5), 1719-1750, doi: 10.3758/s13423-021-02053-1