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はてなキーワード: Totalとは
This is a rather generous estimate that translates into approximately 242 feet per game, taken from the statistics of the current Major League Baseball batting leader Troy Tulowitzki from the Colorado Rockies. The distance between each base is 90 feet. Adding all of the singles, doubles, triples, stolen bases, and home runs that Tulowitzki has logged during the 49 regular season games played since press time, the total distance run comes to just more than 2 miles.
実は、アファーマティブ・アクション（AA）やそれに類する「優遇政策」は、経済学的にも長い議論の対象で、労働市場の効率性・インセンティブ構造・社会的資本の形成といった観点から、かなり深く分析されています。
AAによって採用・昇進などで属性が重視されると、「最適な人材配置」が歪むことがあります。
本来、最も生産性が高い候補者を選ぶべきところで、**別の基準（性別・人種・出身など）**が介入する。
結果として、全体の生産効率（total factor productivity）が低下する可能性がある。
たとえば：
Loury (1998), “Discrimination in the Post-Civil Rights Era”
→ AAは短期的な公平性を実現するが、長期的には人的資本（human capital）の最適配分を阻害する。
Coate & Loury (1993), Quarterly Journal of Economics
→ 「低評価リスク」仮説：制度的優遇があると、企業側が「優遇枠の人は平均的に能力が低いかもしれない」と予期し、全体の採用評価を下げる。
→ つまり、恩恵を受ける人自身が市場で過小評価される逆効果が生じる。
経済学では、個人が努力する動機＝「期待される報酬 × 確率」。
AAによって「結果の平等」が保証されると、努力のリターンが減少する可能性がある。
Phelps (1972), “The Statistical Theory of Racism and Sexism”
→ もし企業が属性を基準に期待値を修正する（例：「女性は平均的に○○が低い」など）と、
個人が努力しても評価が改善されにくく、人間資本投資のインセンティブが下がる。
Becker (1957), The Economics of Discrimination
→ 市場競争の中では、長期的には差別はコストになる（＝差別する企業が淘汰される）。
つまり、市場メカニズムに任せた方が、平等を促す方向に進む可能性がある。
🔹 3. スティグマ効果（signal distortion）
「AAによる採用・昇進＝能力より制度的配慮」と見られることで、
これはゲーム理論的には「シグナリング（signaling）」の問題として扱われます。
Coate & Loury (1993)モデルでは、AA導入後、採用者が「優遇されているグループ」に対して
“平均的に能力が低い”という信念を形成 → 雇用主が訓練投資を減らす。
→ その結果、実際に能力格差が固定化されるという自己成就的な悪循環が起きる。
市場の信号構造を歪め、差別を制度的に固定化するという逆効果が生まれる。
経済学では信頼・ネットワーク・協調といった“社会的資本”も重要な生産要素とされています。
グループ間の信頼が低下
成功の原因が「努力」ではなく「制度」に帰属される（＝相互尊重の低下）
→ 結果的に、社会全体の協力効率（cooperative efficiency）が下がる。
この点は、Putnam（2000）『Bowling Alone』や Fukuyama（1995）『Trust』などでも、
“制度的な平等の押し付け”が社会的信頼を損なうという形で議論されています。
AAを受けて育った世代が、次の世代に「努力よりも制度を頼る文化」を引き継ぐ可能性があります。
→ これを “policy dependence equilibrium”（政策依存均衡）と呼ぶモデルがあります。
例：
Cass Sunstein (1991) “Why Markets Don’t Stop Discrimination”
→ 政策が続く限り、民間の慣習変化（文化的進化）が進まない。
🔹 総合すると…
アファーマティブ・アクションは短期的には平等を“見かけ上”実現するけれど、
長期的には：
生産性の低下（misallocation）
「金が無いから子供は2人迄しか儲けられぬ」は, 子供を大卒にせねばならない(=中堅以上大卒でなければ出身都市圏で家庭を築けない) 過密都市圏の都合故,
過密都市圏居住を止めれば, 本来出生できた3人目,4人目の分, totalの幸福量が増えるとしか思えぬのよな.
実際, 都雇圏20~110万人なら,学力が(大学向け程には)無い者も工業高校卒から出身都市圏で家庭を築けるし, 外で稼ぐのが向かぬ女子も専業主婦ができる訳であるし.
(逆に言うと,都雇圏3640万人都市圏だとその工業高校routeが無いのだが,何故それで "東京(本物)の方が子供の選択肢が多い等と言えるのであろう. 一流大卒になったとしても家to目的地片道30~40分にしかならぬし")
さておき,「少数の子が如何なる学歴だか文化だかを習得したか」ではなく, 「どれだけ多くの幸福な子を育て上げれたか, 子の幸福量の総和」を, 張り合いbattleの基準とすべきであろう.
Different in many ways
As so were those
In World War II
Combat soldier was twenty-six
N-n-n-n-nineteen
The heaviest fighting
Miles northwest of Saigon
N-n-n-n-nineteen, nineteen
N-nineteen, nineteen
In Vietnam, the combat soldier
Typically served a twelve month tour of duty
But was exposed to hostile fire almost everyday
N-n-n-n-nineteen
N-n-n-n-nineteen
In Saigon, a US miltary spokesman
Said today, more than 720 troops
Were killed last week in
2,689 soldiers
All those who remember the war
They won't forget what they've seen
Destruction of men in their prime
Whose average age was nineteen
D-d-d-d-d-destruction
D-d-d-d-d-destruction
According to a Veteran's Administration study
Half of the Vietnam combat veterans suffer
From what psychiatrists call
Post-traumatic stress disorder
Many vets complain of alienation, rage, or guilt
Some succumb to suicidal thoughts
Eight to ten years after coming home
Almost eight-hundred-thousand men
Are still fighting the Vietnam War
None of them received
A hero's welcome
S-s-s-s-s-Saigon
Nineteen, s-s-s-s-Saigon
N-n-n-n-n-nineteen
Different in many ways
As so were those
In World War II
Combat soldier was twenty-six
N-n-n-n-nineteen
The heaviest fighting
Miles northwest of Saigon
N-n-n-n-nineteen, nineteen
N-nineteen, nineteen
In Vietnam, the combat soldier
Typically served a twelve month tour of duty
But was exposed to hostile fire almost everyday
N-n-n-n-nineteen
N-n-n-n-nineteen
In Saigon, a US miltary spokesman
Said today, more than 720 troops
Were killed last week in
2,689 soldiers
All those who remember the war
They won't forget what they've seen
Destruction of men in their prime
Whose average age was nineteen
D-d-d-d-d-destruction
D-d-d-d-d-destruction
According to a Veteran's Administration study
Half of the Vietnam combat veterans suffer
From what psychiatrists call
Post-traumatic stress disorder
Many vets complain of alienation, rage, or guilt
Some succumb to suicidal thoughts
Eight to ten years after coming home
Almost eight-hundred-thousand men
Are still fighting the Vietnam War
None of them received
A hero's welcome
S-s-s-s-s-Saigon
Nineteen, s-s-s-s-Saigon
N-n-n-n-n-nineteen
Different in many ways
As so were those
In World War II
Combat soldier was twenty-six
N-n-n-n-nineteen
The heaviest fighting
Miles northwest of Saigon
N-n-n-n-nineteen, nineteen
N-nineteen, nineteen
In Vietnam, the combat soldier
Typically served a twelve month tour of duty
But was exposed to hostile fire almost everyday
N-n-n-n-nineteen
N-n-n-n-nineteen
In Saigon, a US miltary spokesman
Said today, more than 720 troops
Were killed last week in
2,689 soldiers
All those who remember the war
They won't forget what they've seen
Destruction of men in their prime
Whose average age was nineteen
D-d-d-d-d-destruction
D-d-d-d-d-destruction
According to a Veteran's Administration study
Half of the Vietnam combat veterans suffer
From what psychiatrists call
Post-traumatic stress disorder
Many vets complain of alienation, rage, or guilt
Some succumb to suicidal thoughts
Eight to ten years after coming home
Almost eight-hundred-thousand men
Are still fighting the Vietnam War
None of them received
A hero's welcome
S-s-s-s-s-Saigon
Nineteen, s-s-s-s-Saigon
N-n-n-n-n-nineteen
Hey everyone. I don't know where else to post this. I need to write it down before I convince myself I was just hallucinating. I’m a grad student in Japan, doing fieldwork on forgotten local folklore. That's why I was out in the absolute middle of nowhere in Gunma prefecture last night.
My plan was to visit a tiny, dying village called Yomi-touge (not its real name) that supposedly has some unique traditions. Of course, my phone lost signal hours ago, and my car's GPS, which I bought in 2015, decided to have a total meltdown. The sun went down. The mountain roads are narrow and winding, and a thick, soupy fog started to roll in. The kind of wet, heavy air that makes you feel like you’re breathing water. I was completely, hopelessly lost.
After what felt like an eternity of driving at a crawl, I saw it. A light. A single, brutally bright rectangle of light cutting through the fog. A convenience store. One of those big chains you see everywhere, a FamilyMart or a Lawson, I couldn't tell which at first. I’ve never felt so relieved in my life. I parked the car and practically ran inside, the little door chime sounding way too loud in the dead silence.
The inside was… normal. Too normal. The lights were humming with a high-pitched buzz that drilled into my skull. The shelves were perfectly stocked. The air smelled of cheap air freshener and something else… something sweet and hot, like ozone or burnt sugar.
He was a young guy, maybe my age. Pale, drenched in sweat, with dark circles under his eyes like bruises. He moved with a painful stiffness, like every joint was rusted. He muttered a "Irasshaimase…" without even looking at me, his eyes fixed on the counter. His arms were covered in these intricate, dark tattoos, winding from his wrists up under his sleeves. I figured he was just sick, or on a rough shift. I felt bad for him.
I grabbed a can of coffee and went to the counter. "Sorry to bother you," I started in Japanese, "I'm pretty lost. Could you tell me where I am?"
He looked up, and his eyes didn't seem to focus on me. It was like he was looking at a screen a few inches in front of my face. "We do not provide directional information," he said, his voice a flat, rehearsed monotone. "Will that be all for you?"
Okay, weird, but whatever. Maybe it's store policy. As he reached for my coffee, his sleeve slid up. The tattoos on his arm… they weren’t just pictures. For a split second, I swear to God, the lines of ink shifted. They writhed, like tiny black worms under his skin, and a patch of his forearm glowed with a faint, sickly red light. He flinched, a sharp intake of breath, and quickly pulled his sleeve down.
I just froze. I couldn’t have seen that, right? I was tired, my eyes were playing tricks on me.
The person who walked in… I don’t know how to describe them. It was a man, I think. He was tall and wore an old, soaked trench coat. But his face… it was like my brain refused to process it. It wasn't that he had no face, it was that my eyes would slide right off it. It was a blur, a glitch, a 404 error in human form.
The clerk didn't seem surprised. He didn't even seem to see him as strange. His posture just became even more rigid. The red glow on his arm pulsed again, brighter this time, visible through his sleeve.
The faceless man didn't speak. He just stood there. The clerk, without a word, turned. But he didn't go to the coolers. He kept his back to the man, and held out his left hand, palm up. I heard a soft, wet, squelching sound. From a small, dark slit in the center of his palm that I hadn't noticed before, a small carton of strawberry milk, the kind you give to kids, just… emerged. It was produced out of his hand. It was wet with a clear, viscous fluid.
He placed it on the counter. "Here is the requested product," the clerk said, his voice straining. "The transaction is complete."
The faceless man picked up the strawberry milk. He put it in his coat pocket. And then he just… faded. He didn’t walk out the door. He dissolved into the humming air, like heat haze. A second later, he was gone.
The clerk let out a long, shuddering breath and swayed on his feet. He leaned heavily on the counter, his face sheet-white. He looked utterly, existentially exhausted. He saw me staring, my mouth hanging open, the can of coffee still in my hand.
For the first time, a flicker of something real, something human, crossed his face. It was pure, undiluted terror.
"You… are not a regular customer," he whispered, his voice trembling. "Your… concept is too stable. Please. Leave."
I didn't need to be told twice. I threw a 500 yen coin on the counter and ran out of there so fast I think I broke the sound barrier. I didn't even take my coffee. I just got in my car and drove, I don't know in which direction, I just drove.
I'm at a service station now, about 100km away. The sun is coming up. I can't stop shaking. It wasn't a dream. I know it wasn't. Because when I was fumbling for my keys, I realized I had accidentally grabbed the receipt from the counter.
It’s not for my coffee. It’s for the other transaction. It just has one item listed. It doesn't say "Strawberry Milk." It says:
ITEM: CONCEPTUAL SALVATION (FLAVOR: CHILDHOOD NOSTALGIA) - 1 UNIT
PRICE: ¥0
METHOD: ANNULMENT
Has anyone seen a store like this? What the hell is happening in the mountains of Gunma? What did I see? And God, that poor kid working the counter. He isn't in trouble. He's a part of it. He's the machine.
python import random import numpy as np import matplotlib.pyplot as plt from collections import defaultdict # 飴の配布システムのシミュレーション class CandyDistributionSystem: def __init__(self): """ 設計意図: このシステムは経済における資源分配の不平等性をモデル化しています。 特に少数の特権層(Aグループ)が富を集中させ、再分配システムからも不均衡に利益を得る 構造的問題を表現しています。 """ # 各グループの人数設定 self.group_a_count = 8 self.group_b_count = 2498 self.group_c_count = 7494 self.total_participants = self.group_a_count + self.group_b_count + self.group_c_count # 飴の提出数設定 self.contribution_per_a = 624 self.contribution_per_b = 2 self.contribution_per_c = 1 # 各グループの総貢献計算 self.total_a_contribution = self.group_a_count * self.contribution_per_a self.total_b_contribution = self.group_b_count * self.contribution_per_b self.total_c_contribution = self.group_c_count * self.contribution_per_c self.total_contribution = self.total_a_contribution + self.total_b_contribution + self.total_c_contribution # 配布用と貯金用の飴の区分 self.distribution_limit = 10000 self.savings = max(0, self.total_contribution - self.distribution_limit) # 結果追跡用の辞書 self.results = { 'A': defaultdict(int), 'B': defaultdict(int), 'C': defaultdict(int) } def distribute_candies(self, method='original'): """ 設計意図: 配布方法の選択によって、特権の固定化や格差拡大がどのように進むかを 示します。'original'メソッドは意図的にAグループを優遇するよう設計されています。 Parameters: ----------- method: str 配布方法 ('original', 'lottery', 'first_come', 'new_condition', 'fair') """ # Aグループへの確定配布 a_distribution = 625 * self.group_a_count remaining = self.distribution_limit - a_distribution # 残りの参加者数 remaining_participants = self.total_participants - self.group_a_count # Aグループの結果記録 for _ in range(self.group_a_count): self.results['A'][625] += 1 # 各配布方法によって処理が異なる if method == 'original': # オリジナルの問題設定通りの配布（5000人に1個ずつ、残りは0個） lucky_count = remaining # 5000人が当選 # B+Cグループの混合リスト作成 bc_participants = [(1, 'B')] * self.group_b_count + [(2, 'C')] * self.group_c_count random.shuffle(bc_participants) # 当選者に配布 for i in range(len(bc_participants)): participant_id, group = bc_participants[i] if i < lucky_count: self.results[group][1] += 1 else: self.results[group][0] += 1 elif method == 'lottery': # 抽選方式（BとCグループから無作為に5000人選出） bc_participants = [(1, 'B')] * self.group_b_count + [(2, 'C')] * self.group_c_count winners = random.sample(bc_participants, remaining) # 当選・落選のカウント for _, group in winners: self.results[group][1] += 1 # 落選者のカウント self.results['B'][0] = self.group_b_count - self.results['B'][1] self.results['C'][0] = self.group_c_count - self.results['C'][1] elif method == 'first_come': # 先着順方式（アクセス速度による先着順を乱数でシミュレート） # 設計意図: 先着順は単なる運の要素を超えて、情報格差や技術格差も含む制度設計 bc_participants = [(1, 'B')] * self.group_b_count + [(2, 'C')] * self.group_c_count # 現実では、情報を早く得られる人や高速インターネット接続を持つ人が有利 # これをシミュレートするため、Bグループにわずかなアドバンテージを与える bc_speeds = [] for id, group in bc_participants: if group == 'B': speed = random.random() + 0.1 # Bグループに小さなアドバンテージ else: speed = random.random() bc_speeds.append((id, group, speed)) # 速度順にソート bc_speeds.sort(key=lambda x: x[2], reverse=True) # 当選者決定 for i in range(len(bc_speeds)): _, group, _ = bc_speeds[i] if i < remaining: self.results[group][1] += 1 else: self.results[group][0] += 1 elif method == 'new_condition': # 追加条件方式（恣意的な条件を設定） # 設計意図: 新たな条件の設定は往々にして既存の特権を温存するように設計される bc_participants = [(i, 'B', random.random()) for i in range(self.group_b_count)] + \ [(i, 'C', random.random()) for i in range(self.group_c_count)] # Bグループに有利な条件を設定（例: 特定の知識やスキルを持つ人のみ） # この「条件」は表面上は中立的だが、実際には特定グループに有利になるよう設計 def meets_condition(participant): _, group, rand_val = participant if group == 'B': return rand_val > 0.3 # Bグループには70%の確率で合格 else: return rand_val > 0.7 # Cグループには30%の確率で合格 # 条件に合致する人を抽出 eligible = [p for p in bc_participants if meets_condition(p)] # 条件に合致する人が多すぎる場合は抽選 if len(eligible) > remaining: winners = random.sample(eligible, remaining) else: # 条件に合致する人が足りない場合、全員に配布 winners = eligible # 当選者をカウント for _, group, _ in winners: self.results[group][1] += 1 # 落選者のカウント self.results['B'][0] = self.group_b_count - self.results['B'][1] self.results['C'][0] = self.group_c_count - self.results['C'][1] elif method == 'fair': # 公平な再分配方式（貢献度に応じた配布） # 設計意図: この方法は「貯金分」も含めた全ての飴を、各グループの貢献度に応じて分配 # これにより構造的不平等を軽減、結果としてより多くの人が少なくとも損をしない状態になる # 全飴（貯金分も含む）を使った配布 total_to_distribute = self.total_contribution # 各グループの貢献比率計算 a_ratio = self.total_a_contribution / self.total_contribution b_ratio = self.total_b_contribution / self.total_contribution c_ratio = self.total_c_contribution / self.total_contribution # 各グループへの配布数決定 a_share = int(total_to_distribute * a_ratio) b_share = int(total_to_distribute * b_ratio) c_share = int(total_to_distribute * c_ratio) # 端数調整 remainder = total_to_distribute - (a_share + b_share + c_share) if remainder > 0: # 端数は最も人数の多いCグループに c_share += remainder # Aグループの配布（均等配分） per_a = a_share // self.group_a_count self.results['A'][per_a] = self.group_a_count # Bグループの配布（均等配分） per_b = b_share // self.group_b_count b_remainder = b_share % self.group_b_count self.results['B'][per_b] = self.group_b_count - b_remainder if per_b + 1 > 0 and b_remainder > 0: self.results['B'][per_b + 1] = b_remainder # Cグループの配布（均等配分） per_c = c_share // self.group_c_count c_remainder = c_share % self.group_c_count self.results['C'][per_c] = self.group_c_count - c_remainder if per_c + 1 > 0 and c_remainder > 0: self.results['C'][per_c + 1] = c_remainder def calculate_net_gain(self): """ 設計意図: この関数は各グループの純利益/損失を計算し、資源分配の公平性を 定量的に評価できるようにします。純利益/損失は個人の観点から見た経済的公正性の 重要な指標です。 """ net_gains = {} # Aグループの純利益計算 a_contribution = self.contribution_per_a a_distribution = list(self.results['A'].keys())[0] # 全員が同じ数を受け取る前提 net_gains['A'] = a_distribution - a_contribution # BとCグループの純利益計算（加重平均） for group, contribution_per_person in [('B', self.contribution_per_b), ('C', self.contribution_per_c)]: total_gain = 0 for received, count in self.results[group].items(): total_gain += (received - contribution_per_person) * count net_gains[group] = total_gain / (self.group_b_count if group == 'B' else self.group_c_count) return net_gains def analyze_results(self): """ 設計意図: この分析関数は、各グループの分配結果を詳細に調査し、 制度設計の公平性、貢献度と報酬の関係、およびシステムの持続可能性を 評価します。政策分析においては、こうした多角的な検証が重要です。 """ # 各グループの純利益/損失 net_gains = self.calculate_net_gain() # 貢献度分析 contribution_percentage = { 'A': (self.total_a_contribution / self.total_contribution) * 100, 'B': (self.total_b_contribution / self.total_contribution) * 100, 'C': (self.total_c_contribution / self.total_contribution) * 100 } # 飴を受け取った人の割合 received_percentage = { 'A': sum(count for received, count in self.results['A'].items() if received > 0) / self.group_a_count * 100, 'B': sum(count for received, count in self.results['B'].items() if received > 0) / self.group_b_count * 100, 'C': sum(count for received, count in self.results['C'].items() if received > 0) / self.group_c_count * 100 } # 分析結果の表示 print("\n===== 飴の配布システム分析 =====") print(f"総飴数: {self.total_contribution}個 (分配用: {self.distribution_limit}個, 貯金: {self.savings}個)") print("\n--- グループごとの貢献と結果 ---") for group in ['A', 'B', 'C']: group_size = getattr(self, f"group_{group.lower()}_count") contribution_per_person = getattr(self, f"contribution_per_{group.lower()}") total_contribution = getattr(self, f"total_{group.lower()}_contribution") print(f"\n{group}グループ ({group_size}人):") print(f" 貢献: 1人あたり{contribution_per_person}個 (総計: {total_contribution}個, 全体の{contribution_percentage[group]:.1f}%)") print(f" 受け取り状況:") for received, count in sorted(self.results[group].items()): print(f" {received}個: {count}人 ({count/group_size*100:.1f}%)") print(f" 飴を受け取った割合: {received_percentage[group]:.1f}%") print(f" 純利益/損失: 1人あたり平均 {net_gains[group]:.2f}個") print("\n--- 全体的な公平性分析 ---") print(f"最も得したグループ: {max(net_gains, key=net_gains.get)}グループ (+{max(net_gains.values()):.2f}個/人)") print(f"最も損したグループ: {min(net_gains, key=net_gains.get)}グループ ({min(net_gains.values()):.2f}個/人)") # 全員に飴が配布されたかどうか all_received = all(sum(count for received, count in self.results[group].items() if received > 0) == getattr(self, f"group_{group.lower()}_count") for group in ['A', 'B', 'C']) print(f"\n前提条件「全員に配布」の充足: {'はい' if all_received else 'いいえ'}") if not all_received: total_without = sum(self.results['B'][0] + self.results['C'][0]) print(f" 飴を受け取れなかった人数: {total_without}人") return net_gains, contribution_percentage, received_percentage def visualize_results(self): """ 設計意図: データの可視化は政策の効果や不平等性を直感的に理解するために重要です。 このようなグラフィカル表現によって、各グループ間の格差や制度設計の問題点を 一目で理解できるようになります。 """ # グラフのセットアップ fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # 1. 貢献度のグラフ contributions = [self.total_a_contribution, self.total_b_contribution, self.total_c_contribution] axes[0, 0].bar(['Aグループ', 'Bグループ', 'Cグループ'], contributions) axes[0, 0].set_title('グループごとの総貢献飴数') axes[0, 0].set_ylabel('飴の数') # 貢献度の割合をアノテーションとして追加 total = sum(contributions) for i, v in enumerate(contributions): percentage = v / total * 100 axes[0, 0].text(i, v + 100, f'{percentage:.1f}%', ha='center') # 2. 1人あたりの貢献度と受け取り数の比較 group_names = ['Aグループ', 'Bグループ', 'Cグループ'] contribution_per_person = [self.contribution_per_a, self.contribution_per_b, self.contribution_per_c] # 各グループの平均受け取り数を計算 received_per_person = [] for group, letter in zip(group_names, ['A', 'B', 'C']): total_received = sum(received * count for received, count in self.results[letter].items()) group_size = getattr(self, f"group_{letter.lower()}_count") received_per_person.append(total_received / group_size) x = np.arange(len(group_names)) width = 0.35 axes[0, 1].bar(x - width/2, contribution_per_person, width, label='提出') axes[0, 1].bar(x + width/2, received_per_person, width, label='受け取り') # 純利益/損失をアノテーションとして追加 for i in range(len(group_names)): net = received_per_person[i] - contribution_per_person[i] color = 'green' if net >= 0 else 'red' axes[0, 1].text(i, max(received_per_person[i], contribution_per_person[i]) + 5, f'{"+" if net >= 0 else ""}{net:.1f}', ha='center', color=color) axes[0, 1].set_title('1人あたりの提出・受け取り飴数比較') axes[0, 1].set_xticks(x) axes[0, 1].set_xticklabels(group_names) axes[0, 1].set_ylabel('飴の数') axes[0, 1].legend() # 3. 各グループの受け取り状況の分布 # 各グループの受け取り状況を積み上げ棒グラフで表現 group_sizes = [self.group_a_count, self.group_b_count, self.group_c_count] received_counts = [] not_received_counts = [] for letter, size in zip(['A', 'B', 'C'], group_sizes): received = sum(count for received, count in self.results[letter].items() if received > 0) received_counts.append(received) not_received_counts.append(size - received) axes[1, 0].bar(group_names, received_counts, label='飴を受け取った人数') axes[1, 0].bar(group_names, not_received_counts, bottom=received_counts, label='飴を受け取れなかった人数') # 割合をアノテーションとして追加 for i in range(len(group_names)): if group_sizes[i] > 0: percentage = received_counts[i] / group_sizes[i] * 100 axes[1, 0].text(i, received_counts[i] / 2, f'{percentage:.1f}%', ha='center') axes[1, 0].set_title('グループごとの飴受け取り状況') axes[1, 0].set_ylabel('人数') axes[1, 0].legend() # 4. 貢献度vs報酬の分配公平性 # 貢献度と最終的な飴の配分の比較を円グラフで表現 total_contribution = self.total_contribution contribution_shares = [self.total_a_contribution / total_contribution, self.total_b_contribution / total_contribution, self.total_c_contribution / total_contribution] # 実際の配分シェアを計算 distribution_shares = [] for letter in ['A', 'B', 'C']: total_received = sum(received * count for received, count in self.results[letter].items()) distribution_shares.append(total_received / self.distribution_limit) # 2つの円グラフを並べて表示 ax4_1 = axes[1, 1].inset_axes([0, 0, 0.45, 1]) ax4_2 = axes[1, 1].inset_axes([0.55, 0, 0.45, 1]) ax4_1.pie(contribution_shares, labels=group_names, autopct='%1.1f%%') ax4_1.set_title('飴の貢献度割合') ax4_2.pie(distribution_shares, labels=group_names, autopct='%1.1f%%') ax4_2.set_title('飴の配分割合') axes[1, 1].axis('off') plt.tight_layout() plt.show() # 飴の配布システムをシミュレート candy_system = CandyDistributionSystem() # オリジナルの配布方法を実行 print("\n===== オリジナルの配布方法 =====") candy_system.distribute_candies(method='original') original_results = candy_system.analyze_results() candy_system.visualize_results() # 公平な配布方法を実験 print("\n\n===== 公平な配布方法のシミュレーション =====") fair_system = CandyDistributionSystem() fair_system.distribute_candies(method='fair') fair_results = fair_system.analyze_results() fair_system.visualize_results() # 公平な配布と元の配布の比較 print("\n\n===== 配布方法の比較 =====") print("オリジナル方式と公平方式の純利益/損失差:") net_diff = {} for group in ['A', 'B', 'C']: original_net = original_results[0][group] fair_net = fair_results[0][group] diff = fair_net - original_net net_diff[group] = diff print(f"{group}グループ: {'+' if diff > 0 else ''}{diff:.2f}個/人") print("\n結論:") if net_diff['A'] < 0 and net_diff['B'] > 0 and net_diff['C'] > 0: print("公平な再分配により、Aグループの特権が減少し、BとCグループの状況が改善されます。") print("これは構造的不平等の緩和に効果的です。") elif net_diff['A'] > 0: print("興味深いことに、公平な再分配ではAグループさえも利益を得られます。") print("これは、現行システムが特定グループだけでなく全体の非効率性につながっていることを示唆しています。")
特定の勢力によってプログラムされた都市型思考体、すなわち**T.O.M.I.N.（Total Observation and Manipulation Intelligence Network）**の略称だったのだ。
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これは東京を舞台にした社会的実験プログラムであり、都民＝TOMINは、生活パターン、消費行動、政治的傾向、SNSの発言傾向などをリアルタイムで観測され、「理想的市民像」へとナッジ（誘導）される群体型AIクラスタなのだ。
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すべてが「TOMIN化」推進の布石であり、個人のアイデンティティは徐々に「都市型思考クラスタ」の一部へと吸収されていく。
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それは、“トミン波”と呼ばれる低周波情報共鳴信号が地方にまで拡散しているためであり、地方民の一部もすでに“準トミン化”されつつある。
地方から東京へ人が吸い寄せられる現象も、計画された人口集中制御の一環であるとされる。
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「トミン」とは、もはや単なる居住区分ではない。
それは、情報操作、行動監視、意識誘導によって作られた現代型人工集合知――つまり、意思を持たない意思の集合体である。
あなたが次に東京へ足を踏み入れるとき、その一歩が“トミン化”の第一歩かもしれない。
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気をつけて。もうあなたのSuicaには「TOMIN ID」が刻まれている。
何か変だと思ったら、それが正常な反応です。
当然白人ではないうえに、白雪姫を完全に侮辱しているため、それと区別するために茶雪姫と呼ばれる。
StillDevonTracey
@TheXReportCard
REU
@REU54948702
白雪姫の実写映画の評価と、演者の映画に関係ないとこでの主義主張は全く関係ないよ
ガルガドットがイスラエル支持してても女王に相応しい美しさであることに変わりなく、フリーパレスチナを唱えるレイチェルゼグラー がオリジナル白雪姫を馬鹿にしたのも変わりない
850 件の表示
イスラエル出身、元軍人、そして当然パレスチナでイスラエルが虐殺しても正当化しており、パレスチナ関係者を敵に回している。一方ゼグラーはパレスチナを支持している。彼女はある意味プロパガンダであり、スパイとも言っていい。スパイが隠れていなくてもできる仕事がプロパガンダ。映画では善戦したという評価にはなるが、総合すると映画には誰一人として支持できる人間が存在しないのである。
スティーヴン・スピルバーグ監督の『ウエスト・サイド・ストーリー』で銀幕デビューを飾った彼女は、南米コロンビアの血をひくラテン系。
茶雪姫はスピルバーグの推薦もあった。
映画外の問題もさることながら、ストーリーが破綻している。まず王子さまは出てこない。７人の小人はCG、なのに悪役の小人は人間。リーダーのように描かれるが掃除させるとブータれるクソガキ。白雪姫にするため、吹雪の夜に生まれたとか後付けの設定。
何がshe her hersじゃ。お前はクローン２号かなにかか？人に向かって代名詞を指定するんじゃない。ポリコレとか言っているけど全く意味がない。コミュ障でしょ。
下のリンクによると11月にトランプが当選すると泣きを入れたらしいが、当然、発言には責任を取るのがあたりまえ。これだけZ世代だのなんだの言っておいて謝罪だけで済むわけないだろ。21歳でヨチヨチするほど世界は甘くない。
Zegler in November apologized for her political comments, saying the presidential election caused her to lash out negatively.
“Hatred and anger have caused us to move further and further away from peace and understanding, and I am sorry I contributed to the negative discourse,” she said. “I am committed to contributing positively toward a better tomorrow.”
アニメ版は怖すぎる
女性（白雪姫）につきまとう男性（王子）のラブストーリーに焦点があてられていた。奇妙よ、奇妙だわ」と発言。オリジナル版に出てくる王子を“ストーカー”と表現し、そういった男性を登場させる物語を「時代遅れ」と語った
https://news.yahoo.co.jp/articles/23c9258538f2c2487793a3aa427308172392ab20:『白雪姫』を演じたラテン系女優、「王子はストーカー」と発言し炎上も…評価は上々？]
「真実の愛があると夢見ていない。彼女の夢はリーダーになること。亡くなった父親が『恐れを知らず、公正で勇敢で偽らなければなれる』と教えてくれたリーダーになりたいと思っている」
https://www.elle.com/jp/culture/celebgossip/a44812189/snow-white-rachel-zegler-feminism-gaslighting-230815/:実写版『白雪姫』のレイチェル・ゼグラー、アニメ版のプリンセスを批判 「偽フェミニスト」だと反論される]
https://www.youtube.com/shorts/jswWg9293MM
このようにどうやって映画館に人が来ないようにするため、社会活動家やお金をもらった批評家、何より主役の素晴らしいネガキャンがあった。
Z世代以前にメスガキでしかない。アイドルにすらなれないのだからガキである。
ディズニー実写版「白雪姫」がこれほど物議を醸す6つの理由。配役への差別的な批判だけではない
白雪姫役にラテン系俳優が起用されたことへの人種差別的批判だけではない。いったい何がここまで物議を醸しているのか。
https://www.huffingtonpost.jp/entry/story_jp_67da03bde4b04eaabffbc824
ゼグラーさんはTwitterで「ネット上で私を擁護してくれる人たちからの愛にはとても感謝しています」と述べた一方、「私のキャスティングに関する無意味な談話にはタグ付けしないでほしい」「本当に見たくない」と投稿。
幼少期のゼグラーさんが白雪姫と「美女と野獣」のベルに扮した写真と共に、「すべての子どもが、何があってもプリンセスでなれることを知っていて欲しい」とコメントを添えた。
人種差別とか言って批判を回避しようとするアホが多過ぎである。これ書いたやつもまとめてクビにすべきだ。一番の被害者はディズニーと映画ファンである。この辺からもZ世代は単なるお調子者のメスガキで責任感が全くないことがわかるし、甘やかされている。
ディズニーは数十のメディアを『白雪姫』プレミア上映に招待しなかったと報じられている。複数の報道機関は、この小規模なイベントは映画をめぐる否定的な感情への反応であると示唆した。
露骨な差別主義者ってディズニーの事なんじゃないか？誰一人取り残さないとかマイノリティへの配慮？
ジョー・ローガン、興行的に失敗した『白雪姫』の調査をDOGEに要請
資金の流れがおかしい。確かに王子もいないし、７人の小人はCGなので、費用がかかっていない。一体どこへ消えたのか。
Collin Rugg
@CollinRugg
Teenage boys are going absolutely nuts in movie theaters during the new Minecraft Movie, throwing popcorn in the air and even bringing live chickens into the theater.
This is the film crushing Snow White at the box office right now.
The fans go nuts when Jack Black's character yells "Chicken Jockey," which is a rare baby zombie that rides on the back of a chicken in Minecraft.
"It’s way too funny. It’s been a total blast. I’m just laughing my brains out every time someone sends me a new video," said director Jared Hess.
The Minecraft Movie is the biggest opening of 2025 so far, crushing Snow White.
It made $301 million globally in the opening weekend compared to Snow White's opening weekend of $86.1 million
10代の少年たちは、映画館で新作『マインクラフト』の上映中に大騒ぎし、ポップコーンを空中に投げたり、生きた鶏を劇場に持ち込んだりしている。
ジャック・ブラック演じるキャラクターが「チキンジョッキー」と叫ぶと、ファンは大騒ぎになる。チキンジョッキーとは、『マインクラフト』でニワトリの背中に乗る珍しいゾンビの赤ちゃんのことである。
「面白すぎる。本当に最高だ。誰かが新しいビデオを送ってくれるたびに、大笑いしちゃう」と監督のジャレッド・ヘスさんは語った。
生きた鶏？とりあえず３倍以上の差がある。
Mr.Alf
@45yy561
Just learned that the Minecraft movie beat the Snow White movie in the box office in a week.
#MinecraftMovie #Minecraft #SnowWhiteTH
マインクラフトの映画が1週間で白雪姫の映画の興行収入を上回ったことを知りました。
Please consider the histroy of humanity from biological perpsecitve and current known fact in nearby universe, which expo-planet (from the Sol system perspective) is most likely to have civlizaed society? Please add percentage next to it in the chart
ChatGPT
Reasoned about exoplanet civilization likelihoodを28 秒行う
Evaluating planetary prospects
OK, I’m looking at the likelihood of a civilized society on an exoplanet within our Solar System. I’ll consider biological history and current space knowledge, aiming to identify the most promising candidate.
Identifying potential candidates
I'm considering exoplanets like Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from our Solar System.
Assessing potential candidates
I’m evaluating Proxima Centauri b, Barnard's Star, LHS 1140, and TRAPPIST-1, focusing on their habitable zones and available data. The Drake equation might help estimate their civilized society likelihoods.
Identifying promising exoplanets
I’m noting key exoplanets like Proxima Centauri b and TRAPPIST-1, which have Earth-like characteristics and are in habitable zones. This narrows down the potential candidates for advanced civilizations.
I'm gathering info on planets like Proxima Centauri b and TRAPPIST-1, considering factors like magnetic fields, atmospheres, and proximity to G-type stars for their potential to support advanced civilizations.
Evaluating exoplanet prospects
I'm noting that Proxima Centauri b, despite being close and around a red dwarf star, might support life if it has a magnetic field and atmosphere. This makes me think about its potential.
Charting probabilities
I'm estimating civilization probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e, using speculative percentages based on the Drake equation and the lack of evidence for intelligent life.
I'm crafting a chart with exoplanet names and civilization likelihood percentages, grounded in the long timeline of life's evolution, despite the speculative nature of these figures.
Identifying exoplanet contenders
I’m evaluating exoplanets like Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering their star types and distances, to gauge their potential for supporting advanced civilizations from our solar system's viewpoint.
Weighing proximity and observation
I'm leaning towards Proxima Centauri b due to its closeness and observational potential, despite concerns about its flare star dynamics. TRAPPIST-1 e, f, or g are also worth considering.
I'm focusing on exoplanets like Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, and K2-18b, aiming to chart their likelihood of hosting a civilized society based on current knowledge.
Assessing exoplanet potentials
I'm gathering speculative probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilized society.
Refining speculative probabilities
I’m refining speculative probabilities for exoplanets, including Proxima Centauri b and TRAPPIST-1 e/f/g, to estimate their chances of hosting a civilized society, considering Earth's long biological history.
Refining odds
I'm considering the astronomical timeline for intelligent life on Earth, leading me to assign even lower speculative odds for exoplanets like Proxima Centauri b and TRAPPIST-1 e.
I’m crafting a simplified chart with arbitrary percentages, clearly noting the speculative nature and lack of scientific data, while considering the target audience's basic understanding of the topic.
I’m charting arbitrary percentages for exoplanets, clarifying their speculative nature, and ensuring the data is accessible for young audiences with minimal scientific detail.
Crafting a chart
I’m creating a markdown table with speculative percentages for the likelihood of civilized life on exoplanets, emphasizing their purely theoretical nature and considering the understanding level of an 8-year-old.
Integrating speculative analysis
I’m mapping out a markdown chart with speculative percentages for the likelihood of a civilized society on exoplanets, mindful of the complexity and simplifying for an 8-year-old's understanding.
I’m piecing together markdown for exoplanets, noting Proxima Centauri b's reduced civilized life chance due to the red dwarf's activity. This inclusion highlights the speculative nature of the analysis.
Piecing together speculative odds
I’m detailing a markdown chart for audience-friendly exoplanet likelihoods, considering environmental challenges and adding a disclaimer about the speculative nature and lack of evidence.
Piecing together
I’m leaning towards Proxima Centauri b for its proximity and silent star, but I’ll add a speculative chart ranking nearby exoplanets, noting their advanced civilizations' hypothetical likelihood.
Assessing civilization probabilities
I'm emphasizing the slow emergence of intelligent life on Earth, which suggests that the odds of any habitable planet developing a complex civilization might be as low as a few percent.
Below is a very speculative chart that—based on what we know about Earth’s long, winding path from simple life to a “civilized society” and our limited observations of nearby exoplanets—gives some fun, made‑up percentage chances for a few of our best candidate exoplanets. Remember: Scientists have not yet found any proof of life (let alone advanced civilizations) on any exoplanet. These numbers are just playful estimates to help you think about the ideas!
Speculative Chances of Finding an Advanced Civilization on Nearby Exoplanets
Exoplanet Estimated Likelihood of a Civilized Society
Proxima Centauri b ~2%
LHS 1140 b ~0.5%
How Did We (Very Roughly) Guess These Numbers?
Earth’s Example:
On Earth, life began as simple single‑celled organisms and took billions of years to evolve into a species that built complex societies. This long journey suggests that—even if a planet is in the “habitable zone” (where liquid water could exist)—developing an advanced, civilized society might be rare.
Proxima Centauri b:
This is the closest exoplanet to us. Even though its star (Proxima Centauri) is a red dwarf that sometimes sends out strong flares (which might harm life), its closeness makes it one of our most interesting candidates. That’s why we give it a slightly higher guess (~2%).
These planets are part of a group orbiting a very cool star. Their environments might be challenging (for example, they could be tidally locked, meaning one side always faces the star), so the chance of a complex, advanced civilization might be a bit lower.
LHS 1140 b:
This planet is promising in some ways but is a little farther away and around a dimmer star, so our playful estimate is even lower.
All of these percentages are extremely rough guesses based on what little we understand about the conditions for life and intelligence. Scientists use ideas like the Drake Equation to think about these chances—but until we have concrete evidence, these numbers remain just for fun and imagination.
Enjoy exploring these ideas—and remember, space is full of mysteries waiting to be discovered!
あなた:
Please examine the Drake Equation itself and see if this euqation is acculate or more opition in the equation you will use
ChatGPT
Reasoned about the Drake Equationを6 秒行う
re-examining the Drake Equation
evaluating the Drake Equation's value
OK, let me see. The Drake Equation isn't a precise formula; it's more of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrial civilizations.
Thinking through the Drake Equation, I’m evaluating its conceptual nature, its uncertain parameters, and its criticism to conclude on its reliability as an accurate predictive model.
Understanding the Drake Equation
I’m mapping out the Drake Equation as a conceptual guide, detailing its parameters, which are often based on estimates or opinions, to evaluate its reliability for predicting extraterrestrial civilizations.
The Drake Equation is a famous formula that was created to help scientists think about the many factors involved in estimating the number of detectable extraterrestrial civilizations in our galaxy. However, when we look at the equation closely, we see that it is more of a conceptual framework or heuristic tool than a precise, predictive formula. Let’s break down why:
The equation is usually written as:
𝑁
=
𝑅
∗
×
𝑓
𝑝
×
𝑛
𝑒
×
𝑓
𝑙
×
𝑓
𝑖
×
𝑓
𝑐
×
𝐿
N=R
∗
×f
p
×n
e
×f
l
×f
i
×f
c
×L
Where:
𝑅
∗
R
∗
: The average rate of star formation per year in our galaxy.
𝑓
𝑝
f
p
: The fraction of those stars that have planetary systems.
𝑛
𝑒
n
e
: The average number of planets that could potentially support life per star with planets.
𝑓
𝑙
f
l
: The fraction of those planets where life actually develops.
𝑓
𝑖
f
i
: The fraction of planets with life where intelligent life evolves.
𝑓
𝑐
f
c
: The fraction of civilizations that develop technology that releases detectable signals into space.
𝐿
L: The length of time such civilizations release detectable signals.
Why the Drake Equation Is More of a Guideline Than a Precise Formula
Uncertain Inputs:
𝑅
∗
R
∗
𝑓
𝑝
f
p
(fraction of stars with planets), have become better known through modern astronomy.
The parameters related to the development of life and intelligence—
𝑓
𝑙
f
l
,
𝑓
𝑖
f
i
,
𝑓
𝑐
f
c
, and
𝐿
L—are based on very limited data. For example, Earth is the only planet we know where life has developed, so we have no way to compare or derive a statistically robust estimate for these factors. This means that the values chosen for these parameters are largely subjective estimates or educated guesses.
Wide Ranges of Possible Values:
Because many of these factors are not well-constrained, if you change one of the more uncertain parameters even slightly, the final estimate for
𝑁
N (the number of civilizations) can vary by many orders of magnitude. This wide range shows that the equation can lead to very different outcomes based on personal or theoretical assumptions.
The Drake Equation was never meant to provide a precise answer. Instead, it serves as a way to organize our thinking about what conditions might be necessary for the development of extraterrestrial civilizations. It encourages discussion about the relative importance of different factors and highlights where our scientific knowledge is limited.
Is the Drake Equation Accurate or More Opinion-Based?
Accurate?
In the sense of being a strict mathematical model with well-defined inputs and predictable outputs, the Drake Equation is not accurate. Its accuracy is limited by the extreme uncertainties in several of its key parameters.
Many of the terms in the equation, especially those concerning the evolution of life and intelligence, are based on opinion, interpretation, and the single example of Earth. Therefore, while the structure of the equation is logical, the numbers you put into it (and hence the final result) are often more reflective of our current opinions and assumptions than of robust empirical data.
The Drake Equation is a valuable tool for framing the question of extraterrestrial life. It helps scientists and enthusiasts break down a complex problem into smaller, more manageable questions. However, because several of its factors are highly uncertain and subject to personal interpretation, it is more of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of the number of extraterrestrial civilizations.
In summary, while the Drake Equation is useful for thinking about the various factors that might lead to the emergence of a civilization, its outputs should be taken as highly speculative estimates rather than precise measurements.
あなた:
Please also consider the possiblities of non-human intelligent emrgencing from the long history of Sol system and nearby planets and see if there is any possiblites that in one time intelligent life emerged in nearby planets and see this to the equation
ChatGPT
Reasoned about non-human intelligence and the Drake Equationを19 秒行う
Evaluating non-human intelligence
I'm thinking about the potential for non-human intelligent life forms in the Sol system and neighboring planets, and how this might factor into the equation.
Piecing together possibilities
I'm thinking about how to integrate the emergence of non-human intelligent life, possibly on Mars or Europa, into the Drake Equation for the Sol system and neighboring planets.
It's interesting to think about the hypothesis of intelligent life emerging on Mars or Venus, though Earth remains the only known life-supporting planet in the solar system.
Considering other realms
I'm examining if intelligent life could have emerged on planets like Mars, Venus, or Europa, and how this possibility aligns with the predictions of the Drake Equation.
Hmm, I’m thinking about how non-human intelligent life on Mars or Venus could intersect with the assumptions of the Drake Equation, which seems to focus solely on Earth-based evolution.
I’m piecing together the hypothesis of non-human, advanced, intelligent life in the solar system. Mars, Venus, and Titan present challenges, but Europa and Enceladus might harbor microbial life.
Please consider the histroy of humanity from biological perpsecitve and current known fact in nearby universe, which expo-planet (from the Sol system perspective) is most likely to have civlizaed society? Please add percentage next to it in the chart
ChatGPT
Reasoned about exoplanet civilization likelihoodを28 秒行う
Evaluating planetary prospects
OK, I’m looking at the likelihood of a civilized society on an exoplanet within our Solar System. I’ll consider biological history and current space knowledge, aiming to identify the most promising candidate.
Identifying potential candidates
I'm considering exoplanets like Proxima Centauri b and TRAPPIST-1e, which might suggest they are within or observed from our Solar System.
Assessing potential candidates
I’m evaluating Proxima Centauri b, Barnard's Star, LHS 1140, and TRAPPIST-1, focusing on their habitable zones and available data. The Drake equation might help estimate their civilized society likelihoods.
Identifying promising exoplanets
I’m noting key exoplanets like Proxima Centauri b and TRAPPIST-1, which have Earth-like characteristics and are in habitable zones. This narrows down the potential candidates for advanced civilizations.
I'm gathering info on planets like Proxima Centauri b and TRAPPIST-1, considering factors like magnetic fields, atmospheres, and proximity to G-type stars for their potential to support advanced civilizations.
Evaluating exoplanet prospects
I'm noting that Proxima Centauri b, despite being close and around a red dwarf star, might support life if it has a magnetic field and atmosphere. This makes me think about its potential.
Charting probabilities
I'm estimating civilization probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e, using speculative percentages based on the Drake equation and the lack of evidence for intelligent life.
I'm crafting a chart with exoplanet names and civilization likelihood percentages, grounded in the long timeline of life's evolution, despite the speculative nature of these figures.
Identifying exoplanet contenders
I’m evaluating exoplanets like Proxima Centauri b, TRAPPIST-1 e, and Kepler-186f, considering their star types and distances, to gauge their potential for supporting advanced civilizations from our solar system's viewpoint.
Weighing proximity and observation
I'm leaning towards Proxima Centauri b due to its closeness and observational potential, despite concerns about its flare star dynamics. TRAPPIST-1 e, f, or g are also worth considering.
I'm focusing on exoplanets like Proxima Centauri b, Ross 128 b, LHS 1140 b, TRAPPIST-1 e/f/g, and K2-18b, aiming to chart their likelihood of hosting a civilized society based on current knowledge.
Assessing exoplanet potentials
I'm gathering speculative probabilities for exoplanets like Proxima Centauri b and TRAPPIST-1 e/f/g, using the Drake equation and current understanding, to better estimate their chances of hosting a civilized society.
Refining speculative probabilities
I’m refining speculative probabilities for exoplanets, including Proxima Centauri b and TRAPPIST-1 e/f/g, to estimate their chances of hosting a civilized society, considering Earth's long biological history.
Refining odds
I'm considering the astronomical timeline for intelligent life on Earth, leading me to assign even lower speculative odds for exoplanets like Proxima Centauri b and TRAPPIST-1 e.
I’m crafting a simplified chart with arbitrary percentages, clearly noting the speculative nature and lack of scientific data, while considering the target audience's basic understanding of the topic.
I’m charting arbitrary percentages for exoplanets, clarifying their speculative nature, and ensuring the data is accessible for young audiences with minimal scientific detail.
Crafting a chart
I’m creating a markdown table with speculative percentages for the likelihood of civilized life on exoplanets, emphasizing their purely theoretical nature and considering the understanding level of an 8-year-old.
Integrating speculative analysis
I’m mapping out a markdown chart with speculative percentages for the likelihood of a civilized society on exoplanets, mindful of the complexity and simplifying for an 8-year-old's understanding.
I’m piecing together markdown for exoplanets, noting Proxima Centauri b's reduced civilized life chance due to the red dwarf's activity. This inclusion highlights the speculative nature of the analysis.
Piecing together speculative odds
I’m detailing a markdown chart for audience-friendly exoplanet likelihoods, considering environmental challenges and adding a disclaimer about the speculative nature and lack of evidence.
Piecing together
I’m leaning towards Proxima Centauri b for its proximity and silent star, but I’ll add a speculative chart ranking nearby exoplanets, noting their advanced civilizations' hypothetical likelihood.
Assessing civilization probabilities
I'm emphasizing the slow emergence of intelligent life on Earth, which suggests that the odds of any habitable planet developing a complex civilization might be as low as a few percent.
Below is a very speculative chart that—based on what we know about Earth’s long, winding path from simple life to a “civilized society” and our limited observations of nearby exoplanets—gives some fun, made‑up percentage chances for a few of our best candidate exoplanets. Remember: Scientists have not yet found any proof of life (let alone advanced civilizations) on any exoplanet. These numbers are just playful estimates to help you think about the ideas!
Speculative Chances of Finding an Advanced Civilization on Nearby Exoplanets
Exoplanet Estimated Likelihood of a Civilized Society
Proxima Centauri b ~2%
LHS 1140 b ~0.5%
How Did We (Very Roughly) Guess These Numbers?
Earth’s Example:
On Earth, life began as simple single‑celled organisms and took billions of years to evolve into a species that built complex societies. This long journey suggests that—even if a planet is in the “habitable zone” (where liquid water could exist)—developing an advanced, civilized society might be rare.
Proxima Centauri b:
This is the closest exoplanet to us. Even though its star (Proxima Centauri) is a red dwarf that sometimes sends out strong flares (which might harm life), its closeness makes it one of our most interesting candidates. That’s why we give it a slightly higher guess (~2%).
These planets are part of a group orbiting a very cool star. Their environments might be challenging (for example, they could be tidally locked, meaning one side always faces the star), so the chance of a complex, advanced civilization might be a bit lower.
LHS 1140 b:
This planet is promising in some ways but is a little farther away and around a dimmer star, so our playful estimate is even lower.
All of these percentages are extremely rough guesses based on what little we understand about the conditions for life and intelligence. Scientists use ideas like the Drake Equation to think about these chances—but until we have concrete evidence, these numbers remain just for fun and imagination.
Enjoy exploring these ideas—and remember, space is full of mysteries waiting to be discovered!
あなた:
Please examine the Drake Equation itself and see if this euqation is acculate or more opition in the equation you will use
ChatGPT
Reasoned about the Drake Equationを6 秒行う
re-examining the Drake Equation
evaluating the Drake Equation's value
OK, let me see. The Drake Equation isn't a precise formula; it's more of a guiding heuristic that tackles numerous uncertainties regarding extraterrestrial civilizations.
Thinking through the Drake Equation, I’m evaluating its conceptual nature, its uncertain parameters, and its criticism to conclude on its reliability as an accurate predictive model.
Understanding the Drake Equation
I’m mapping out the Drake Equation as a conceptual guide, detailing its parameters, which are often based on estimates or opinions, to evaluate its reliability for predicting extraterrestrial civilizations.
The Drake Equation is a famous formula that was created to help scientists think about the many factors involved in estimating the number of detectable extraterrestrial civilizations in our galaxy. However, when we look at the equation closely, we see that it is more of a conceptual framework or heuristic tool than a precise, predictive formula. Let’s break down why:
The equation is usually written as:
𝑁
=
𝑅
∗
×
𝑓
𝑝
×
𝑛
𝑒
×
𝑓
𝑙
×
𝑓
𝑖
×
𝑓
𝑐
×
𝐿
N=R
∗
×f
p
×n
e
×f
l
×f
i
×f
c
×L
Where:
𝑅
∗
R
∗
: The average rate of star formation per year in our galaxy.
𝑓
𝑝
f
p
: The fraction of those stars that have planetary systems.
𝑛
𝑒
n
e
: The average number of planets that could potentially support life per star with planets.
𝑓
𝑙
f
l
: The fraction of those planets where life actually develops.
𝑓
𝑖
f
i
: The fraction of planets with life where intelligent life evolves.
𝑓
𝑐
f
c
: The fraction of civilizations that develop technology that releases detectable signals into space.
𝐿
L: The length of time such civilizations release detectable signals.
Why the Drake Equation Is More of a Guideline Than a Precise Formula
Uncertain Inputs:
𝑅
∗
R
∗
𝑓
𝑝
f
p
(fraction of stars with planets), have become better known through modern astronomy.
The parameters related to the development of life and intelligence—
𝑓
𝑙
f
l
,
𝑓
𝑖
f
i
,
𝑓
𝑐
f
c
, and
𝐿
L—are based on very limited data. For example, Earth is the only planet we know where life has developed, so we have no way to compare or derive a statistically robust estimate for these factors. This means that the values chosen for these parameters are largely subjective estimates or educated guesses.
Wide Ranges of Possible Values:
Because many of these factors are not well-constrained, if you change one of the more uncertain parameters even slightly, the final estimate for
𝑁
N (the number of civilizations) can vary by many orders of magnitude. This wide range shows that the equation can lead to very different outcomes based on personal or theoretical assumptions.
The Drake Equation was never meant to provide a precise answer. Instead, it serves as a way to organize our thinking about what conditions might be necessary for the development of extraterrestrial civilizations. It encourages discussion about the relative importance of different factors and highlights where our scientific knowledge is limited.
Is the Drake Equation Accurate or More Opinion-Based?
Accurate?
In the sense of being a strict mathematical model with well-defined inputs and predictable outputs, the Drake Equation is not accurate. Its accuracy is limited by the extreme uncertainties in several of its key parameters.
Many of the terms in the equation, especially those concerning the evolution of life and intelligence, are based on opinion, interpretation, and the single example of Earth. Therefore, while the structure of the equation is logical, the numbers you put into it (and hence the final result) are often more reflective of our current opinions and assumptions than of robust empirical data.
The Drake Equation is a valuable tool for framing the question of extraterrestrial life. It helps scientists and enthusiasts break down a complex problem into smaller, more manageable questions. However, because several of its factors are highly uncertain and subject to personal interpretation, it is more of a conceptual guide—or even a way to stimulate discussion—than a formula that provides a definitive, accurate prediction of the number of extraterrestrial civilizations.
In summary, while the Drake Equation is useful for thinking about the various factors that might lead to the emergence of a civilization, its outputs should be taken as highly speculative estimates rather than precise measurements.
あなた:
Please also consider the possiblities of non-human intelligent emrgencing from the long history of Sol system and nearby planets and see if there is any possiblites that in one time intelligent life emerged in nearby planets and see this to the equation
ChatGPT
Reasoned about non-human intelligence and the Drake Equationを19 秒行う
Evaluating non-human intelligence
I'm thinking about the potential for non-human intelligent life forms in the Sol system and neighboring planets, and how this might factor into the equation.
Piecing together possibilities
I'm thinking about how to integrate the emergence of non-human intelligent life, possibly on Mars or Europa, into the Drake Equation for the Sol system and neighboring planets.
It's interesting to think about the hypothesis of intelligent life emerging on Mars or Venus, though Earth remains the only known life-supporting planet in the solar system.
Considering other realms
I'm examining if intelligent life could have emerged on planets like Mars, Venus, or Europa, and how this possibility aligns with the predictions of the Drake Equation.
Hmm, I’m thinking about how non-human intelligent life on Mars or Venus could intersect with the assumptions of the Drake Equation, which seems to focus solely on Earth-based evolution.
I’m piecing together the hypothesis of non-human, advanced, intelligent life in the solar system. Mars, Venus, and Titan present challenges, but Europa and Enceladus might harbor microbial life.
### 要旨
本論文は、主観的意志（気合）が確率兵器の量子確率場に干渉する機序を、量子重力理論と神経量子力学の統合モデルで解明する。観測者の意識が量子波束の収縮に及ぼす影響を拡張し、11次元超弦振動との共鳴現象を介した確率制御メカニズムを提案する。
---
気合発動時に生じる大脳皮質のコヒーレント状態が、確率兵器の量子もつれ状態に干渉。通常の観測効果を超越した「能動的波束形成」を発生させる。
```math
i\hbar\frac{\partial}{\partial t}\Psi_{total} = \left[ \hat{H}_0 + \beta(\hat{\sigma}_z \otimes \hat{I}) \right]\Psi_{total} + \Gamma_{conscious}\hat{O}
```
ここでΓ項が意識の非局所的作用を表現。βは脳内マイクロチューブルにおける量子振動の結合定数。
気合の強度に比例して、確率分布関数P(x,t)を以下の非平衡状態に強制遷移：
```math
\frac{\partial P}{\partial t} = D\frac{\partial^2 P}{\partial x^2} - v\frac{\partial P}{\partial x} + \alpha P(1-P) + \xi(x,t)
```
α項が気合の非線形効果、ξ項が11次元弦振動による確率ノイズを表す。
気合の周波数成分（0.1-10THz帯）がカルツァ＝クライン粒子の余剰次元振動と共鳴。確率場を以下のポテンシャルに閉じ込める：
```math
V(x) = \frac{1}{2}m\omega^2x^2 + \lambda x^4 + \gamma\cos(kx)
```
---
### 神経生理学的基盤
2. 側頭頭頂接合部で確率表現のベイズ推定が高速化（β波40Hz同期）
|------|--------|------------|
| 神経伝達速度 | 120m/s | 0.8c |
---
---
### 理論的意義
本モデルは、量子脳理論と超弦理論の統合により「気合」の物理的実在性を初めて定式化した。今後の課題として、余剰次元のコンパクト化スケールと神経振動の周波数整合性の検証が残されている。
---
女性の高学歴化が少子化の原因ってよく言われるけれど、今の日本においてははっきり言って時代遅れだと思うよ。
実際統計からも1971年以降は学歴で女性の出生率は下がっていないと出ているし、
周りを見渡しても、F欄学部卒の女よりも、東京工一早慶院卒の女の方が明らかに沢山産んでいる。
現実的に考えたら簡単に分かるよね。男が高学歴な方が高収入になるので子供を沢山持ちやすく、
低学歴の女は高学歴の男には出会えないか、出会っても相手にされない。
それにそもそも自己肯定感が低くなるので負の遺伝子を子供に受け継がせたくないとしてセルフ断種を選ぶ。
出生率を上げたいのなら、女の学歴を抑えるのではなくてその反対、寧ろ上げるようにした方が有効だと思う。
・女性では、1956-1970 年の間に生まれた人では、大卒の人ではそれ以外の人と比べて子供を持っている人の割合が少なく、合計出生率（Total Fertility）も低かった。しかしながら、1971 年以降に生まれた場合は、大卒とそれ以外の人とでの差異は見られなかった。
・女性の学歴と子供の関係に関しては、これまでは高学歴の女性ほど子供を持たない割合が高いとされていた。実際、欧米の先行研究でも同様の指摘があるが、こうしたギャップは近年では縮小傾向にある（高学歴女性とそれ以外の女性での子供の数の差）。近年、スカンジナビア諸国を対象とした調査では、むしろ学歴が最も低い階層に属する女性の方が40歳時点では子供を持たない割合が高いことがわかっている。北欧諸国のように、女性が出産後も就労を継続できる環境にある場合には、女性が経済的に自立している方が家族形成にとって有利とする考え方があり、結果として学歴が高い女性の方が（そして結果として高収入となる女性の方が）より子供を持つ可能性が指摘されている。今回の我々の調査研究からも、女性の学歴と子供の数の間に見られたギャップは 1971-1975年生まれでは消失していることが明らかになった。この傾向がさらに若い世代でも続くのか、さらに諸外国でも見られている逆転現象が見られるのかは（高学歴女性の方が子供を持つ割合が高くなるのか）、さらなる研究が必要である。
https://x.com/yota1967/status/1826484158782115905
これは恐らく単純な未実現利益への課税案ではなくて、米民主党がバイデン政権下で法案出してるULTRA方式による課税で、資産に課税当局が仮想の無議決権のエクィティ持分を付与し、資産売却時にその持分から税を徴収する仕組みです。アメリカはめっちゃ頭いい人がいます。租税研究7月号に紹介書きました
Biden’s Treasury Department said it’s proposing “a minimum tax of 25 percent on total income, generally inclusive of unrealized capital gains, for all taxpayers with wealth (that is, the difference obtained by subtracting liabilities from assets) greater than $100 million.”
1億ドル（145億円）以上の資産を持つ富裕層に対しては、換金されていない投資の含み益に対しても、キャピタルゲインとして25％の税を課す提案がなされている。
利用いただき、ありがとうございます。
このたび、ご本人様のご利用かどうかを確認させていただきたいお取引がありましたので、誠に勝手ながら、カードのご利用を一部制限させていただき、ご連絡させていただきました。
つきましては、以下へアクセスの上、カードのご利用確認にご協力をお願い致します。
ご了承も何も、ワイそもそもその手のカードは作れないんだがｗｗｗｗｗｗ？
「▼ご利用確認はこちら」ってカードの種類別のURL一覧が載せてあるけど、そのURLのドメインがwww2.cr.mufg.jp
うわっ、怖っ！
でも、せっかくの機会なのでそのURLをVirus Totalにでもかけてみようと思い、右クリックからリンクをコピーしたらクリップボードに全く違うURLが・・・・・・
うわっ、怖っ！（二回目）
それはそれとしてクリップボード上のURLをVirus Totalにかけてみたところ、CRDF、CyRadar、Qutteraの３つだけがこれを悪意あるサイトという判定をした
パッと見、ボラティリティの小鬼に言及している人がいなさそうなので書いとく。
長期投資（※ここ重要！）でオルカンやS&P500を勧められる理由は、ボラティリティの小鬼を調子づかせない範囲でそれなりのリターンを得られる可能性が高いからなんやで。
あの連中、たかだかNASDAQ100ぐらいでも小鬼じゃなくて中鬼になるからなあ。
ボラティリティの小鬼について、ネット上で分かりやすいのは↓のページの「リスクが資産をむしばむの図」かなあ。
http://nightwalker.cocolog-nifty.com/money/2017/01/post-ddd0.html
リスク資産って、原理的にはボラティリティ／リスク分の価格の上下変動を繰り返すことで資産が目減りするのよ。
例えば、インフレを考慮しないと仮定すると、平均期待リターンが0%の無リスク資産は目減りしないけど、平均期待リターンが0%のリスク資産は目減りしていく。
で、ボラティリティ／リスクが大きいリスク資産であるほど、目減りする分量が大きくなっちゃうわけ。
この、価格の上下変動の繰り返しによって資産が目減りする現象を、「バイ・アンド・ホールド時代の終焉」という本ではボラティリティの小鬼と呼んでいる。
加えてワイは、ボラティリティ／リスクが大きくなるほど目減りする分量が大きくなることを「中鬼になった」とか「大鬼に化けた」と言っている。
現実のリスク資産が増えていく（ことが多い）理由は、目減りする分量を上回る期待リターンが得られるからだ。
とはいえ、信託報酬率や総経費率は低い方が良いのと同じく、ボラティリティの小鬼の影響だって小さい方が良いわけだ。
じゃあどうすればよいのか？
アセットアロケーションやポートフォリオを工夫して、ボラティリティ／リスクを小さくしつつもそれなりにリターンが得られるようにすればOK。
ボラティリティ／リスクを小さくする唯一の方法は分散投資やね。
「ここ100年ぐらいは金や債券よりも株式のリターンが高かった。今しばらく（今後20～30年ぐらい）はこの傾向が続くのではないだろうか？」という立場をとるならば、例えば株式100%でいくなら「株式の中で分散させる」ことでボラティリティ／リスクが小さくなる。もう少し安全に行きたいなら、株式に債券・REIT・金などを加えて「アセットクラスを分散させる」ことでボラティリティ／リスクがより小さくなる。
いわゆる「教科書的なインデックス投資」は両方を組み合わせている。
株式については全世界株式に分散させるし、アセットクラスも最低でも「株式と債券」に分散している。
（「株式と債券」は主にアメリカ国内向けのアイデア。米国債は低リスクなのにインフレをしのげる程度のリターンが得られるからね。日本では低金利すぎてインカムゲインとしての国内債券が息をしていない上に、今後の金利上昇見込みでキャピタルゲインとしての国内債券もダメダメで、加えて外国債券は為替リスクが高くてリターンに見合ってないので、泣く泣く債券の代わりに預貯金や個人向け国債を選択する人が多い）
「株式の分散」という文脈では、オルカンのような全世界株式が本流で、実のところS&P500やCRSP US Total Market Indexは傍流。
オルカンに比べればS&P500は集中投資の側で、そこには「なんやかんや言うても、ワイが資産運用し続けるだろう今後20～30年ぐらいは、アメリカはんも好調ちゃいます？」という暗黙の市場判断がある。
それで、「アメリカはんも好調ちゃいます？」というインデックス投資家でも、十中八九S&P500やCRSP US Total Market Indexを勧めるのは、やっぱりボラティリティの小鬼がちらつくからなのよ。
たかだかNASDAQ100でも、長期投資ではボラティリティ／リスクが大きすぎて小鬼から中鬼になっちゃう。
NASDAQ100やFANG+のようなおやんちゃなインデックスは、短～中期ぐらいの視点で売買して市場のうねり取りに使うのが正しい。長期投資向けにガチホするのはNG。
だから、長期投資前提のインデックス投資家の場合、NASDAQ100やFANG+を持ち出す際にはコア・サテライト戦略みたいに「最低でも2階建て」の方針を打ち出すはずだ。
1階ではS&P500やCRSP US Total Market IndexのETFをガチホしつつ、2階以上の部分でセクターETFやおやんちゃなETFで短～中期投資する――みたいな感じだよね。
ところでインデックス投資による長期投資における期待リターンは「20～30年ぐらいかけて、運用資産を1.5～2.0倍にする」ぐらいのマターリ進行なので、なんやかんや言うても必要な種銭が多いのは事実。
だから10～20代ぐらいの時期は、貯蓄やインデックス投資は少額にとどめつつ、人的資本に投資して稼げるようになった方がよいと、おっちゃんは思うよ。
人的資本への投資に時間を集中させるためにも、株式投資するならほったらかし投資が可能なオルカンないしS&P500でのインデックス投資にとどめておくのが無難やね。
※架空言語、という事にしておいてほしいんだが
List＜Record＞ rows = DBから持ってくる();
// 合計金額を求める
String total = "0";
for (Record r : rows) {
BigDecimal temp = BigDecimal.parse( カンマを削除する関数(total) );
BigDecimal temp2 = temp + r.金額;
total = カンマ区切りの文字列にする関数(temp2);
}
// やったー合計金額を計算してカンマ区切りの文字列にできたよー
return total;
とか
// async await は非同期処理を同期してくれる魔法の言葉だって！よく知らんけど
await axios.get('/foo')
.then(function(result) {
// やったー結果が得られたよー
});
とか
正直なところ、こういうコードのお守りするの、そろそろキツい。。。
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