Author: Jacqueline Wei-Sum Chan

Nash equilibrium and the dominant strategy are standard concepts used to explain game analysis in game theory and is used in many different areas of academic disciplines, such as economics and legal theory.

In the field of construction, game theory has been increasingly applied to analyse a wide variety of problems. Some examples include wage negotiations and optimization of investment projects. In particular, there was a study done on how game theory could be used to help indicate the best winning strategy in court cases between a general contractor (GC) and an investor (IN). The case study is based on a real situation that has happened between two companies in Europe and is going to be analyzed from the GC’s perspective, the party that brought the case to court.

Analogous to choosing strategy A or B in the picture above, both parties GC and IN may take an aggressive approach or a conciliatory strategy in court.

• An aggressive approach is more costly as a lot of resources are going to be used to actively search for opponent’s weaknesses
• A conciliatory approach costs less as it involves searching for a rational compromise and a shorter litigation.

The nitty gritty details about the costs (negative numbers) were based off of the real scenario and can be explained in the linked article, but to summarize, both parties would want to aim for a higher payout.

Additionally, it is realistic to also consider how payoffs and strategies would change depending on court rulings. In the study, it was equally probable for the court to either rule in favour of the GC or the IN. Therefore, two different analysis per court ruling using the Nash equilibrium concept and dominant strategy concept were made. 

Nash Equilibrium for image Table 3, is if GC applies Aggressive Strategy and IN applies Conciliatory Strategy.

Nash Equilibrium for image Table 4, is if GC applies Conciliatory Strategy and IN applies Conciliatory Strategy.

Considering both payoff tables, the IN should apply the conciliatory strategy. It is a more complex answer for what GC should do as different court rulings would impact their strategy. In the case of when the court favours GC, it is best for GC to be aggressive. However, if the ruling is favouring IN, the only strategy for GC to have a positive payoff is conciliatory. 

Although there is not a clear answer as to what GC must always do, it is reasonable to take into other factors on a case to case basis. Depending on how strongly prepared GC is for the court case (concrete evidence, lots of witnesses, expert assessments) or perhaps how ill prepared IN is, if it can be calculated that there is at least a 0.69 probability that such factors will have an impactful influence in the court ruling, then the aggressive strategy is effective. 

The Nash equilibrium and dominant strategy is not only discussed in CSCC46 but also in many areas of study when game theory is often used to analyze scenarios. As discussed in class, the dominant strategy was used here in this scenario to help explain situations where the two parties (GC and IN in this case) might have more of an incentive to choose one option over another. This article provides a real world insight of how such a strategy can be useful for both opponents. In the case of the IN, they are always going to try to minimize their loss whereas in the case of GC, their optimal strategy will depend on the predictions on whether or not the court will rule in their favour. The article discusses other factors to consider, such as when further analysis needs to be done depending on “game states” which was, in the article, the side the court could choose to favour. Despite such thorough analysis, it was also highlighted that legal action should be the last option GC should try to resolve conflicts as there are less expensive alternatives such as mediation. However, if it ever did get to the point of bringing the investors to court, game theory would play an important role in choosing a winning strategy.

References:

Grzyl, B., Apollo, M., & Kristowski, A. (2019). Application of Game Theory to Conflict Management in a Construction Contract. Sustainability, 11(7), 1983. doi: 10.3390/su11071983

Mentioned Blog:

https://www.investopedia.com/terms/n/nash-equilibrium.asp

Software companies such as Neo4j and Cambridge Intelligence, provide network visualization products so that organizations could apply social network techniques to analyze data. They advocate for fraud prevention, and have worked with organizations all over the world, including law enforcement. As credit cards have become an increasingly popular form of payment worldwide, these software companies provide insight on the importance of graphing visualization tools in detecting credit card fraud. This is an interesting topic to discuss because of how widespread the problem has become globally and how complex it is to solve it. Many may argue that preventing all fraud incidents is not possible, as it is similar to the cat and mouse scenario, where both parties will always try to be one step ahead of each other. However, there are many innovative graphing tools today, along with machine learning and artificial intelligence algorithms that analysts can use to help prevent complicated criminal activities that were previously undetectable.

Detecting fraud is difficult because of constantly changing tactics that criminals use and their increasingly sophisticated strategies. Fraudulent activities could generally be categorized as two different kinds, known and unknown fraud. Known fraud is an activity that analysts have identified before and can be detected by defined algorithms and automation. Unknown fraud is an activity that needs to be detected, as it has not been encountered before. 

Using graphing technology, the large amount of data that is often incomplete and complex could be rearranged into a network, which analysts could then use to extrapolate or interpolate patterns and find anomalies that otherwise could go unnoticed.  While many algorithms are able to detect known fraud scenarios, detecting fraud when criminals work together and collaborate prove to be much harder to expose.  

Identifying credit card fraud involves studying the relationships between accounts, transactions, events and people. By displaying these entities in a graph, it would be easier to pinpoint the point of origin and common denominators. Additionally, Cambridge Intelligence provide solutions where noise data could be filtered out to highlight underlying patterns and features to support temporal based analyses.

Numerous graphing analyses and techniques can be used to detect fraud. In the case of credit card fraud, multiple network relationships are created by criminals to hide fraudulent activities. In a network, there may be thousands of credit card users and, for example, a new client might be interested in applying for a credit card. Using graph visualization tools to rearrange data, analysts will be able to identify networks that the client is a part of (community detection). Through identifying related clusters and the connectivity of their network, analysts can determine how likely a client is to be a fraud.  As an example, a basic indicator of potential fraudulent activity is if the client is trying to apply for a credit card with a SIN number that is the same SIN as a few different other accounts, but those accounts are under a different name or already marked as fraud. Thus, an investigation could be initiated. A lot more other attributes could be analyzed (i.e. phone numbers, addresses), but generally it is more simple and fast to follow the network connections than a brute force approach. By doing further investigations, such as calculating the node degrees of the social network, you may be able to find the person with the highest influence or perhaps which specific attributes prove to be the most influential in the network. This is useful because it will help in breaking down potential fraud rings, or preventing one to form. It may be the case where the neighbouring nodes that are one edge away from the client in the graph are not known as frauds. However, further analyses may reveal a few frauds 2 edges away from the client. Further more edges away, and a pattern may start to form and show that the client may be involved in some fraud activity. The goal of these graph analyses and identifying such patterns are to prevent fraud before it occurs.

In a similar way, these graphing analyzation and visualization techniques could be used to detect Insurance fraud and Bank fraud.

Fraud costs businesses billions of dollars each year. As crimes become harder to detect due to the level of complexity and sophistication, it is important for data tools and analyzing techniques to also continue to develop and innovate, with the goal of detecting such fraudulent crimes so it can be prevented.  Graph technology and social network analyses help take part in minimizing such losses.

Article:

Sadowksi, Gorka, and Philip Rathle. “Fraud Detection: Discovering Connections with Graph Databases.” Neo Technology, Neo Technology, Jan. 2015.

Relevant Links:

Credit card fraud visualization: AI detection strategies that work

Data visualization for fraud detection tools

https://neo4j.com/graphconnect-2018/session/graph-technology-ai-machine-learning