Published on: 2026-01-24

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Intelligence Report: The New Misogyny Violent Extremism and What It Will Take to Stop It RSVP for a Live Conversation Online or in LA

1. BLUF (Bottom Line Up Front)

The primary driver of mass shootings in the United States is increasingly linked to gendered grievances and misogyny rather than political extremism alone. This assessment suggests that addressing these root causes is essential for reducing violence. The analysis is made with moderate confidence due to existing information gaps and potential biases in data interpretation.

2. Competing Hypotheses

• Hypothesis A: Rising gendered grievances and misogyny are the primary predictors of mass shootings, as supported by Cynthia Miller-Idriss’ research. This hypothesis is supported by data indicating a consistent link between misogyny and violent extremism, but it is limited by a lack of comprehensive data across all incidents.
• Hypothesis B: Political extremism remains the dominant factor in mass shootings, with gendered grievances being a secondary or correlated factor. This alternative explanation is supported by the historical focus on political motives in extremist violence, but it lacks the nuanced understanding of gender dynamics highlighted in recent studies.
• Assessment: Hypothesis A is currently better supported due to emerging research that highlights misogyny as a consistent predictor across various ideologies. Future data collection and analysis could shift this judgment if broader patterns of political extremism are shown to be more directly causal.

3. Key Assumptions and Red Flags

• Assumptions: Gender-based grievances are a significant factor in radicalization; misogyny is a consistent predictor of violence; current data accurately reflects underlying causes.
• Information Gaps: Comprehensive data on the role of gendered grievances in all mass shooting incidents; longitudinal studies on the evolution of misogyny in extremist movements.
• Bias & Deception Risks: Potential cognitive bias in interpreting data through a gender-focused lens; source bias from advocacy groups with specific agendas; risk of oversimplification in attributing causality.

4. Implications and Strategic Risks

The focus on misogyny as a driver of extremism could lead to new policy approaches and preventive measures. However, it may also polarize public discourse and complicate existing counter-terrorism strategies.

• Political / Geopolitical: Potential for increased legislative focus on gender-based violence; risk of politicization of gender issues in security policy.
• Security / Counter-Terrorism: Need for revised threat assessments and training for law enforcement on gender dynamics in radicalization.
• Cyber / Information Space: Increased online discourse around gender issues; potential for extremist groups to exploit gender grievances in recruitment.
• Economic / Social: Possible shifts in funding towards gender-focused violence prevention programs; societal debates on gender roles and violence.

5. Recommendations and Outlook

• Immediate Actions (0–30 days): Initiate data collection on gender-based grievances in recent mass shootings; engage with gender studies experts for policy input.
• Medium-Term Posture (1–12 months): Develop partnerships with academic institutions for research on misogyny and violence; enhance community outreach programs focusing on gender violence prevention.
• Scenario Outlook:
  • Best: Comprehensive understanding leads to effective policy interventions reducing violence.
  • Worst: Misogyny-driven violence continues to rise, exacerbating social tensions.
  • Most-Likely: Gradual integration of gender-focused strategies into broader counter-terrorism efforts.

6. Key Individuals and Entities

• Cynthia Miller-Idriss, Sociologist and Author
• Kathy Spillar, Executive Editor of *Ms.*
• Polarization and Extremism Research and Innovation Lab (PERIL)

7. Thematic Tags

national security threats, violent extremism, gender-based violence, misogyny, counter-terrorism, radicalization, public policy, social dynamics

Structured Analytic Techniques Applied

• Cognitive Bias Stress Test: Expose and correct potential biases in assessments through red-teaming and structured challenge.
• Bayesian Scenario Modeling: Use probabilistic forecasting for conflict trajectories or escalation likelihood.
• Network Influence Mapping: Map relationships between state and non-state actors for impact estimation.

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