Create a clean, academic-style flowchart illustrating a multi-round competitive game experiment with LLM-driven agents. The diagram should show the following workflow from left to right or top to bottom: Initialization: A population of agents with heterogeneous intrinsic abilities (sampled from a normal distribution). Each agent is assigned a fixed disposable capital E per round. Agents are placed into one of three experimental conditions: No-network condition (isolated agents, no social information) Small-world network Hybrid network combining small-world structure with scale-free degree distribution (scale-free network with added triangles) Repeated Game Loop (T rounds): For each round t: Each agent decides an investment level oᵢ,ₜ ∈ [0, E] Uninvested capital (E − oᵢ,ₜ) is saved as private wealth A global competition mechanism allocates performance scores based on a Boltzmann-style rule, incorporating relative investment, intrinsic ability, and stochastic noise Information Feedback: Agents observe their own recent history (last five rounds), including investments, scores, and rankings. In networked conditions, agents additionally observe the corresponding information of their connected neighbors. Memory and Strategy Update (every five rounds): Agents generate a reflective summary of past performance, reassess their own ability, and formulate a forward-looking strategy. This summary is stored as memory and used as reference information for future decisions. Ranking and Final Outcome: Rankings are determined by cumulative average performance. At the final round, a winner-take-all mechanism is applied: only the top 20% of agents receive a final reward R. Total final wealth equals accumulated saved capital plus the possible final reward. Use simple icons for agents, networks, memory blocks, and reward allocation. The style should be minimal, professional, and suitable for a computational social science or game theory paper.
論文品質のニューラルネットワーク構造図を描画してください：入力は、マルチセンサー漏洩音響波からの2種類の特徴（カオス特徴...
![Create a clean, academic-style flowchart illustrating a multi-round competitive game experiment with LLM-driven agents.
The diagram should show the following workflow from left to right or top to bottom:
Initialization:
A population of agents with heterogeneous intrinsic abilities (sampled from a normal distribution).
Each agent is assigned a fixed disposable capital E per round.
Agents are placed into one of three experimental conditions:
No-network condition (isolated agents, no social information)
Small-world network
Hybrid network combining small-world structure with scale-free degree distribution (scale-free network with added triangles)
Repeated Game Loop (T rounds):
For each round t:
Each agent decides an investment level oᵢ,ₜ ∈ [0, E]
Uninvested capital (E − oᵢ,ₜ) is saved as private wealth
A global competition mechanism allocates performance scores based on a Boltzmann-style rule, incorporating relative investment, intrinsic ability, and stochastic noise
Information Feedback:
Agents observe their own recent history (last five rounds), including investments, scores, and rankings.
In networked conditions, agents additionally observe the corresponding information of their connected neighbors.
Memory and Strategy Update (every five rounds):
Agents generate a reflective summary of past performance, reassess their own ability, and formulate a forward-looking strategy.
This summary is stored as memory and used as reference information for future decisions.
Ranking and Final Outcome:
Rankings are determined by cumulative average performance.
At the final round, a winner-take-all mechanism is applied: only the top 20% of agents receive a final reward R.
Total final wealth equals accumulated saved capital plus the possible final reward.
Use simple icons for agents, networks, memory blocks, and reward allocation.
The style should be minimal, professional, and suitable for a computational social science or game theory paper.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fvw1Gnd9NCyziYwTyT6q2uXK6J2zyJMnT%2F7aa136b9-e4fe-4993-8c83-f2a8e187769a%2F3d05bab2-a68e-48b9-9e18-b9eaa7b0b577.png&w=3840&q=75)
![特徴選択アルゴリズムの段階的な垂直フローチャートを、学術論文の図版スタイル（淡い色、明確な線）で描いてください。プロセスは水平線で区切られた3つの段階に分かれています。
第1段階（高速スクリーニング）：
1. 入力：元の特徴量X、目的変数Y
2. 相互情報量I(X_j; Y)を計算（k近傍法）
3. 相互情報量の降順にソート → F_sorted
4. 選択戦略（並列分岐）：
- 閾値戦略：I ≥ τ
- 個数戦略：上位k = β×d個の特徴量を選択
5. 出力：S1, X1
第2段階（mRMR + Markov Blanket）：
1. S1から開始し、最大の相互情報量を持つ特徴量を選択してSに追加
2. mRMR反復：
- 候補特徴量の限界mRMRスコアを計算
- 最高スコアを選択してSに追加
3. Markov Blanketスクリーニング：
- S内の各特徴量X_iについて：
- I(X_i; X_j) > τ_mb かつ I(X_j; Y) ≥ I(X_i; Y) を満たすX_jを見つける
- 見つかった場合、X_iを削除
4. 出力：S2
第3段階（PCBWOFS最適化）：
1. エンコーディング：バイナリ b=[b_1,...,b_m]
2. 初期化（混合戦略）
3. 適合度評価：適合度 = Acc - α·|b|/m
4. 再生と交叉：
- 選択 → ORゲート交叉 → コンテキストアウェア交叉
5. 共食い戦略：低適合度の個体を排除
6. 突然変異：ビット反転 + 特徴量スワップ
7. エリートを保持しながら、集団を反復的に更新
8. 出力：最適な特徴量サブセット
フローチャート要素の要件：
- ステップは長方形のボックスで、判断/分岐はひし形のボックスで表現
- 矢印は流れの方向を示す
- 「第1/2/3段階」のタイトルを使用して3つの段階をラベル付け
- 全体的なレイアウトは簡潔で、論文への挿入に適していること。](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fhpsp97YaFBIUPx0EbaYDvGh7CgMgwN6k%2F78922486-2288-41ff-b4fd-2ad57a745aa8%2F13bd335c-4284-418b-83a3-f002ed2565b3.png&w=3840&q=75)
