實際經驗表明,決策有好多種唔同方法做,而唔同嘅決策方式達到目的嘅效果都唔同。管理同經濟等商學領域嘅學者好多都想知「點樣做決策,先能夠令自己達到目的嘅機率有咁大得咁大」[2];認知心理學同腦神經學嘅工作者就會想剖析(例如)人做決策嗰陣個腦裏面發生緊乜事[3],而 AI 工作者又會想教電腦好似人腦噉做決策[4]。因為噉,決策呢家嘢吸引咗唔少學術界人士嘅興趣,引起唔同界別嘅研究者對決策相關嘅課題撰文。
AI:廿世紀嗰陣,好多人都諗住 AI 可以齋靠窮舉搜尋[歐 2]做-窮舉搜尋指將啲可能答案逐個逐個睇一次,睇嗮就畀最理想嗰個答案[註 4]出嚟做 output(慢);但打後嘅研究表明,啲人遠遠低估咗啲問題有幾複雜,例如想像家陣要教 AI 捉圍棋,圍棋極之複雜,有成 10170 個可能情況咁多,部電腦運算能力幾勁都唔能夠喺限定時間內計嗮;於是 AI 工作者就改用一啲比較「聰明」嘅做法,佢哋教 AI 淨係揀一部份嘅可能性嚟睇,而且揀起嚟有技巧,揀到大機率會用到嗰啲可能性嚟睇(快),最後搵到嘅答案唔包保係所有可能答案當中最理想嘅,但個答案會「有咁上下理想」而且可以喺相對短嘅時間內計出嚟[12]。例如出名嘅捉圍棋 AI 程式 AlphaGo 就用咗噉嘅做法[13]。
廿一世紀初畀好多人指係資訊爆炸嘅事代:隨住資訊科技愈嚟愈先進,要搜集資訊愈嚟愈易,一個人喺 Google 搜尋器入面是但打幾隻字,就已經會搵到大量相關嘅料,啲料多到佢根本睇唔嗮。心理學同腦神經學等領域上嘅研究表明,人腦嘅資訊處理能力係有限嘅[33],當一個人腦要處理嘅資訊嘅量大得滯(資訊過多),往往會搞到個人唔舒服,而且實驗性質嘅研究仲發現,當要處理嘅資訊嘅量大到咁上下,個人做準確判斷嘅能力會下跌。因為噉,資訊過多同資訊爆炸等嘅課題响決策相關討論上廣受關注[34]。
為咗應對「手上有大拃資訊」嘅問題,2010 年代嘅 IT 界興起咗大數據[歐 10]相關技術。大數據泛指一啲「因為大得滯複雜得滯,廿世紀同打前嗰啲數據處理方法搞唔掂」嘅數據集。處理噉嘅數據會用到機械學習同第啲 AI 技術,呢啲技術重點係要將啲數據簡化[35]:例如想像做市場研究,搵到拃數據返嚟,
於是個 CEO 就決定要用工作滿足感分數(X)做衡量 Y 嘅指標(攞條統計規律做操控),但佢手下嗰啲中層管理人員一聽到呢個政策,就個個都想令自己個部門望落效率高啲,叫自己手下啲員工填問卷嗰陣特登填高啲嘅滿足感分數(啲人操縱 X),最後搞到啲問卷上面嘅工作滿足感分數唔再反映到啲員工做嘢有幾快手(統計規律倒冧)。
協作智能[歐 18]係人工智能(AI)同相關研究上成日有人提到嘅一個概念,指一班彼此知道對方身份嘅智能體-包括人同機械-各自[註 10]噉行事,嚟幫手解決一個佢哋大家都想解決嘅問題。喺廿一世紀初嘅管理學上,有好多工作者都想應對 AI 愈嚟愈普及化嘅形勢,因而有興趣想思考「人應該點樣同 AI 合作,形成一個有協作智能嘅組織」等嘅問題[50][51]。
响廿一世紀初,AI 最勁嘅地方係,能夠以人類冇可能追得上嘅速度睇大量嘅數據[註 11](睇返上面資訊過多嘅應對):AI 能夠(例如)喺幾日以至幾個鐘嘅時間內睇勻嗮數以萬計嘅病人嘅數據,學識邊啲病徵同邊啲病症有關,所以可以攞嚟幫手診斷病人(用「手上病人嘅病徵」預測「佢有乜嘢病」),而且表現仲好過人類嘅醫生[52]-相比之下,人要做同樣嘅嘢就要用閒閒哋幾年嘅時間嚟學習。因為噉,喺多個領域當中都已經出現「管理者用 AI 取代人嚟幫手做預測」噉嘅情況[53]。
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