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Understanding Gaslighting Through Game Theory

A look at gaslighting and its effects using game theory.

― 5 min read


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Gaslighting is a form of manipulation that distorts someone's perception of reality. It often makes individuals doubt themselves and their surroundings, affecting their mental and emotional well-being. In the digital age, gaslighting has become easier to execute, particularly through cyber activities and misinformation. With the use of technology, it is possible for individuals or organizations to spread false information that can change how others perceive reality.

This article discusses a specific way of studying gaslighting using a game theory approach. Game theory looks at situations where different players make decisions that affect each other. In this case, we focus on two players: the gaslighter, who seeks to manipulate perceptions, and the Decision-maker (DM), who tries to make sense of the misleading information.

The Problem of Gaslighting

Gaslighting can occur in various settings, from personal relationships to larger social contexts, including online platforms. It involves the gaslighter creating a false narrative that can lead the DM to second-guess their thoughts and actions. This can have serious consequences, as the DM may make choices based on false information, ultimately harming themselves.

As technology advances, the means to gaslight have also evolved. Cyber attackers can gather information from different sources and create disinformation campaigns. This manipulation can lead people to take actions that serve the gaslighter's interests rather than their own.

Understanding how gaslighters manipulate information to control perceptions is essential for developing strategies to counteract their attempts.

The Game Setup

To analyze gaslighting, we can use a game-theoretic framework called a Stackelberg game. In this type of game, one player (the gaslighter) makes a move first, and the other player (the DM) reacts to that move. The outcomes depend on the actions of both players, which are influenced by their available information.

In our scenario, the gaslighter has complete access to the system's state and can design the observations that the DM receives. On the other hand, the DM can only see the observations provided by the gaslighter, which may be misleading. This difference in information creates an advantage for the gaslighter, who can manipulate the DM's understanding of the situation.

The DM constructs a perception of the environment based on the limited observations available. This is important because the DM’s decisions rely on these perceptions. If the gaslighter can influence these perceptions effectively, they can steer the DM’s actions in a desired direction, all while remaining hidden in the shadows.

Analyzing Gaslighting Effects

The core of this analysis is to examine how much the gaslighter's manipulation affects the DM's understanding and actions. This involves looking at how resilient the DM's perceptions are against gaslighting efforts.

One way to measure the impact of gaslighting is to assess the Robustness of the DM's beliefs. Robustness here refers to the stability of the DM's understanding despite exposure to false information.

For example, if the gaslighter changes the observations to skew the DM's perception, we look at how much this change affects the DM's information state. The more resilient the DM's understanding is, the less impact the gaslighter has.

Measures of Stealthiness

A critical aspect of gaslighting is that it often happens without the DM realizing it. Thus, a measure of stealthiness comes into play. This measure helps ensure that the gaslighter can manipulate perceptions without being detected by the DM.

The stealthiness requirement means that the gaslighter's efforts should be subtle enough to evade the DM’s scrutiny. If the DM can easily recognize that they are being misled, their ability to act will be less affected.

By establishing a standard for stealthiness, we can evaluate if the gaslighter's actions remain under the radar while still achieving their goals of manipulation.

Evaluating the DM's Optimal Value

As the DM reacts to the gaslighter’s actions, they aim to minimize their costs based on their perceptions of the system. Evaluating the DM's optimal value under the influence of gaslighting involves understanding how the manipulation affects their cost assessments.

When the DM is misled, their decision-making process can become skewed, leading to poor outcomes. By analyzing changes in the DM's optimal value-essentially their performance or success based on their choices-we can gauge how effective the gaslighter's efforts are.

Solutions to Counter Gaslighting

Awareness and understanding of gaslighting can aid in developing strategies to mitigate its effects. For the DM, one of the simplest ways to reduce the impact of gaslighting is to lower their trust levels regarding the information they receive. By adopting a more skeptical approach towards the observations provided, the DM can protect themselves from manipulation.

Further, incorporating sensitivity analysis could help the DM understand how variations in information affect their decisions. This approach allows the DM to identify when their perceptions may be shifting due to gaslighting efforts.

Applications of the Framework

The proposed game-theoretic framework can be applied to various situations beyond individual interactions. For example, organizations can use it to analyze the effects of misinformation campaigns during elections or marketing strategies.

In addition, this framework can enhance cybersecurity measures. By understanding how attackers manipulate perceptions and information, organizations can better defend against such tactics. Building systems that identify disinformation and provide alternative perspectives will help reduce vulnerability to gaslighting.

Conclusion

Gaslighting is a substantial issue that can lead to harmful outcomes for individuals and society. By using a dynamic game-theoretic approach, we can better understand the mechanics of gaslighting and its impacts on decision-making. This analysis highlights the importance of information control and perceptions in manipulative strategies.

Through the establishment of stealth and robustness measures, we can assess the effectiveness of gaslighters in manipulating perceptions. Armed with this knowledge, individuals and organizations can create strategies to counteract gaslighting and promote healthier decision-making processes.

In the end, gaining awareness of gaslighting and its implications allows for more informed reactions and potentially reduces its harmful effects. Protecting oneself from manipulation involves critically evaluating the information received and seeking clarity in uncertain situations.

Original Source

Title: On the Impact of Gaslighting on Partially Observed Stochastic Control

Abstract: Recent years have witnessed a significant increase in cyber crimes and system failures caused by misinformation. Many of these instances can be classified as gaslighting, which involves manipulating the perceptions of others through the use of information. In this paper, we propose a dynamic game-theoretic framework built on a partially observed stochastic control system to study gaslighting. The decision-maker (DM) in the game only accesses partial observations, and she determines the controls by constructing information states that capture her perceptions of the system. The gaslighter in the game influences the system indirectly by designing the observations to manipulate the DM's perceptions and decisions. We analyze the impact of the gaslighter's efforts using robustness analysis of the information states and optimal value to deviations in the observations. A stealthiness constraint is introduced to restrict the power of the gaslighter and to help him stay undetected. We consider approximate feedback Stackelberg equilibrium as the solution concept and estimate the cost of gaslighting.

Authors: Shutian Liu, Quanyan Zhu

Last Update: 2023-04-03 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2304.01134

Source PDF: https://arxiv.org/pdf/2304.01134

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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