# Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence

## Abstract

**:**

## 1. Background, Motivation and Contribution

#### 1.1. Motivation

#### 1.2. Contribution

#### 1.3. Extending Single-Agent Optimization Failures

- Tails Fall Apart, or Regressional inaccuracy, where the relationship between the modeled goal and the true goal is inexact due to noise (for example, measurement error,) so that the bias grows as the system is optimized.
- Extremal Model Insufficiency, where the approximate model omits factors which dominate the system’s behavior after optimization.
- Extremal Regime Change, where the model does not include a regime change that occurs under certain (unobserved) conditions that optimization creates.
- Causal Model Failure, where the agent’s actions are based on a model which incorrectly represents causal relationships, and the optimization involves interventions that break the causal structure the model implicitly relies on.

#### 1.4. Defining Multi-Agent Failures

## 2. Multi-Agent Failures: Context and Categorization

#### 2.1. Texas Hold’em and the Complexity of Multi-Agent Dynamics

#### 2.2. Limited Complexity Models versus the Real World

#### 2.3. Failure modes

**Failure Mode**

**1.**

**Accidental Steering**is when multiple agents alter the systems in ways not anticipated by at least one agent, creating one of the above-mentioned single-party overoptimization failures.

**Remark**

**1.**

**Model.**

**1.1—Group Overoptimization.**

**Remark**

**2.**

**Model.**

**1.2—Catastrophic Threshold Failure.**

**Remark**

**3.**

**Example**

**1.**

**Example**

**2.**

**Failure Mode**

**2.**

**Coordination Failure**occurs when multiple agents clash despite having potentially compatible goals.

**Remark**

**4.**

**Model.**

**2.1—Unintended Resource Contention.**

**Remark**

**5.**

**Example**

**3.**

**Remark**

**6.**

**Model.**

**2.2—Unnecessary Resource Contention.**

**Remark**

**7.**

**Failure Mode**

**3.**

**Adversarial optimization**can occur when a victim agent has an incomplete model of how an opponent can influence the system. The opponent’s model of the victim allows it to intentionally select for cases where the victim’s model performs poorly and/or promotes the opponent’s goal [3].

**Model.**

**3.1—Adversarial Goal Poisoning.**

**Example**

**4.**

**Example**

**5.**

**Example**

**6.**

**Remark**

**8.**

**Model.**

**3.2—Adversarial Optimization Theft.**

**Failure Mode**

**4.**

**Input spoofing and filtering**—Filtered evidence can be provided, or false evidence can be manufactured and put into the training data stream of a victim agent.

**Model.**

**4.1—Input Spoofing.**

**Remark**

**9.**

**Example**

**7.**

**Example**

**8.**

**Model.**

**4.2—Active Input Spoofing.**

**Example**

**9.**

**Example**

**10.**

**Model.**

**4.3—Input Filtering.**

**Example**

**11.**

**Remark**

**10.**

**Failure Mode**

**5.**

**Goal co-option**is when an opponent controls the system the Victim runs on, or relies on, and can therefore make changes to affect the victim’s actions.

**Remark**

**11.**

**Model.**

**5.1—External Reward Function Modification.**

**Remark**

**12.**

**Model.**

**5.2—Output Interception.**

**Model.**

**5.3—Data or Label Interception.**

**Example**

**12.**

**Remark**

**13.**

## 3. Discussion

#### Potential Avenues for Mitigation

## 4. Conclusions: Model Failures and Policy Failures

## Funding

## Acknowledgments

## Conflicts of Interest

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Manheim, D.
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence. *Big Data Cogn. Comput.* **2019**, *3*, 21.
https://doi.org/10.3390/bdcc3020021

**AMA Style**

Manheim D.
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence. *Big Data and Cognitive Computing*. 2019; 3(2):21.
https://doi.org/10.3390/bdcc3020021

**Chicago/Turabian Style**

Manheim, David.
2019. "Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence" *Big Data and Cognitive Computing* 3, no. 2: 21.
https://doi.org/10.3390/bdcc3020021