Digital Sandbox Engine for
Future Prediction
Upload any seed material, and millions of AI agents evolve freely in a parallel world, finally giving you a detailed deduction report. It turns the eternal question of "What if..." into an experiment that can be run in code.
Redefining Predictive Science
By building high-fidelity parallel digital worlds, let the future happen in code ahead of time.
Core Methodology: Observing Evolutionary Trajectories
Let things happen in the digital world, observe the actual evolutionary trajectory
Thousands of AI agents with independent personalities, long-term memory, and behavioral logic interact freely in a simulated social platform. Posting, commenting, forwarding, arguing, forming alliances. Users observe public opinion evolution from a "God's eye view" and obtain structured prediction reports.
Parallel Digital World
The Core Form of Swarm Research
Derived from digital twins, not only replicating physical entity states but also building a virtual social system capable of autonomous evolution and intervention experiments.
Executable Social System Copy
Emergence of Complex Social Dynamics
Capturing phenomena such as information cascades, group polarization, and the rise of opinion leaders, faithfully reproduced in multi-agent interaction simulations.
Million-Level Concurrent Simulation
Computational Basis for Macro Social Prediction
Supports concurrent social simulation of up to one million agents, allowing the system to simulate the social network dynamics of a medium-sized city simultaneously.
From Biological Inspiration to High-Order Cognitive Intelligence
Swarm Research applies the biological swarm intelligence paradigm to social prediction, achieving a key innovation from "simple rules emerging complex behaviors" to "cognitive agents emerging social intelligence".
Ant Colony Foraging
Pheromone indirect communication, positive feedback path reinforcement
Ant Colony Optimization (ACO), solving combinatorial optimization
Bird Flocking
Separation-Alignment-Cohesion three local rules
Computer graphics, crowd animation
Bee Colony Decision
Scout bee exploration, waggle dance information integration
Distributed decision making, consensus formation mechanism
Fish School Evasion
Local perception, rapid coordinated turning
Swarm robotics, autonomous driving coordination
Breaking the Black Box, Revealing How Predictions Are Formed
When single AI prediction accuracy might be only 30%, through information aggregation, opinion debate, and evidence weighing among agents, the accuracy of group decision-making can be significantly improved.
- Tracking opinion evolution of key opinion leaders
- Identifying trigger nodes of information cascades
- Quantifying the reinforcement effect of echo chambers
Emergence
Properties exhibited by the system as a whole that cannot be simply deduced from individual rules. This "undesignable but observable" characteristic is the key advantage of swarm intelligence prediction over traditional statistical models.
Micro: Simple Decision Rules
Adaptive interactions of each agent produce unpredictable patterns
Macro Pattern Emergence
macroPublic Opinion Wave
Topic explosion triggered by information cascades
Opinion Polarization
Camp opposition caused by echo chamber effects
Alliance Formation
Spontaneous collaboration brought by convergence of interests
From Unstructured Text to
Interpretablerit Knowledge Graph
The quality of seed materials determines the fidelity of simulation. GraphRAG empowers AI with "logical reasoning" capability by constructing an entity-relationship-attribute knowledge graph.
News Reports
Situation analysis typical materials, including event background, timeline, multiple perspectives and communication data.
Policy Documents
Macro-environment constraints, defining the rules and regulatory boundaries of simulation.
Story Chapters
Complex interpersonal relationships and social context extraction for deep social simulation.
Academic Papers
Expertise domain knowledge injection, ensuring scientific validity and logical rigor.
GraphRAG Core Advantages
Traditional RAG
Split documents into text chunks, match based on semantic similarity.
Fails to handle complex logical relationships across passages
GraphRAG
Build symbolic knowledge graphs, supporting relationship reasoning.
Enables <b>A</b> to support <b>B</b>, <b>B</b> to oppose <b>C</b>, <b>A</b> may indirectly oppose <b>C</b>
Marketing Ontology Should Be Extended
OntologyTemporal Grounded SearchNot just recall, but the pulse of history
On the basis of traditional knowledge graph, adding temporal dimension enables AI to distinguish "past concepts" from "current information", simulating the real cognitive process of updating knowledge and changing attitudes.
- Temporal Tagging
For each fact triple, add a valid time range
- Change Event Detection
Automatically detect entity creation, destruction, attribute changes
- Historical Reasoning
"Since A supported B last week, but now B fell into controversy, how will A respond?"
Define a SMART Research Question
The system automatically analyzes user questions through large language models, extracting research prediction objectives, constraints, and success criteria.
Batch Generate Heterogeneous Agents & Init Environment
The system automatically builds highly complex simulated societies based on graph content, without manual rule configuration.
Demographics
Psychographics
Cognitive Biases
Knowledge & Memory
Emotional State
Social Network Position
Simulated Communities
Agents are deployed into digital platforms that highly restore real-world features, each with unique algorithms.
Single Evolution Cycle
Simulation Parameters
Adjust time and scale to fit different business needs.
Time Span Setting
Sandbox Scale
Economies of Scale Trade-off Larger scale doesn't always mean linear accuracy gain. Research shows macro emergence stabilizes around 5,000 agents. Start small.
Evolution Starts
System engine starts, multi-model collaboration ensures realism.
Voice
- Proactive posting
- Reply to others
- Cross-platform relay
Stance
- Like / Dislike
- Voting
- Emoji reactions
Relations
- Follow new nodes
- Unfollow / Block
- Join sub-groups
Decision
- Purchase intent
- Brand preference reversal
- Join boycotts
Underlying Models
Drives agent thought and behavior, requires high logic and role-play capability.
Simulates platform algorithms and physical world feedback.
Silently records interactions, performs real-time data aggregation.
Memory & Context
Hybrid memory architecture to solve context forgetting in long simulations.
From "Data" to "Actionable Strategy"
ReportAgent has privileges to access global sim data. It transforms millions of micro-interactions into understandable macro patterns.
Data Aggregation
Aggregate individual interactions into explainable patterns
Scenario Recognition
Identify typical evolution paths and key turning points
Anomaly Detection
Mark outlier scenarios and analyze implications
Comparative Analysis
Compare outcomes under different intervention conditions
Standardized Report
Standardized TemplateMulti-dimensional Analysis
Unlike traditional single-perspective analysis, ReportAgent navigates freely between micro motivations and macro trends.
Macro Trend Layer
ChartsTopic lifecycle, sentiment polarization
Meso Structure Layer
TopologyCommunity tears, echo chamber boundaries
Micro Motive Layer
CausalityWhat arguments persuaded the stubborn?
Counterfactual Layer
MatrixWhat if published a day later?
Opinion Evolution Sim
Identify Key Nodes in the Network
Not all nodes are equal. Intervening with a few key nodes yields outsized results.
Info Hubs
Highest betweenness centrality
Accelerate cross-circle spread
Emotional Triggers
Highly susceptible & contagious
Prevent PR crises
Bridges
Weak ties between opposing camps
Break echo chambers, build consensus
Silent Majority
Low interaction but easily influenced
Decides the final public opinion
See your next decision before it happens
Tell us your scenario. We’ll show the workflow and a sample report for your brand.