Prima di Memmo, i miei appunti erano sparsi tra mille PDF. Ora uno spazio di lavoro raccoglie tutto in un unico posto, e vedo esattamente cosa mi resta da studiare.
As the study of environmental policy and justice becomes increasingly significant in today’s global climate, standard statistical approaches to gathering data have become less helpful at generating new insights and possibilities. None of the conventional frameworks easily allow for the empirical modeling of the interactions of all the actors involved, or for the emergence of outcomes unintended by the actors. The existing frameworks account for the "what," but not for the "why."
Heather E. Campbell, Yushim Kim, and Adam Eckerd bring an innovative perspective to environmental justice research. Their approach adjusts the narrower questions often asked in the study of environmental justice, expanding to broader investigations of how and why environmental inequities occur. Using agent-based modeling (ABM), they study the interactions and interdependencies among different agents such as firms, residents, and government institutions. Through simulation, the authors test underlying assumptions in environmental justice and discover ways to modify existing theories to better explain why environmental injustice occurs. Furthermore, they use ABM to generate empirically testable hypotheses, which they employ to check if their simulated findings are supported in the real world using real data.
The pioneering research on environmental justice in this text will have effects on the field of environmental policy as a whole. For social science and policy researchers, this book explores how to employ new and experimental methods of inquiry on challenging social problems, and for the field of environmental justice, the authors demonstrate how ABM helps illuminate the complex social and policy interactions that lead to both environmental justice and injustice.
Prima di Memmo, i miei appunti erano sparsi tra mille PDF. Ora uno spazio di lavoro raccoglie tutto in un unico posto, e vedo esattamente cosa mi resta da studiare.
I riassunti di Memmo sono oro puro prima degli esami. Non devo rileggere 800 pagine due settimane prima, solo le parti importanti.
La chat AI mi ha salvato più di una volta la sera prima di un esame. Continuo a chiedere finché non capisco, senza aspettare risposte da un gruppo di studio.
I quiz colpiscono esattamente ciò che devo sapere. Memmo tiene traccia di dove mi blocco, così mi esercito solo su ciò che conta davvero.
Le flashcard con ripetizione spaziata sono magia pura. Memmo sa quando sto per dimenticare qualcosa e me lo ripropone.
I podcast AI sono i miei preferiti. Li ascolto mentre vado a scuola e ripasso senza stare davanti al computer.
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