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A. Mahdavi:
"Understanding and modeling people's needs and behavior in buildings";
Keynote Lecture: Harvard Graduate School of Design <=> Invited Lecture, Harvard (Graduate School of Design) (invited); 2022-03-11.

engl. table of contents:
Understanding and modeling people's needs and behavior in buildings
Data input classes for performance simulation
Understanding and predicting buildings' performance
People in building performance simulation
Building performance simulation
Occupants' influence on building performance
Instances of passive effects
The issue of resolution
Representation of presence in simulation with discrete intervals
Instances of active effects
The spectrum of formalisms
Representation: states and events
The "gap" and the ensuant fallacies
Generation of random patterns
Plug load models - Use of office equipment
Case study of window operation
When use what model?
Single values versus distributions
The scope of data-driven models
A tradeoff
Cars' fuel economy as a comparative case in point
Another tradeoff
Cars' fuel economy as a comparative case in point
"I am sensitive to sensitivity analysis,
but uncertain about uncertainty analysis."
(keynote speech BS2019 Rome)
Building Performance Data/Indicators
Building performance prediction versus Building performance projection
Building Performance Data Ontology
The dimensions of model application space
When use what model?
"ABM represents decision-making entities, called agents
that individually assess their situation and make descision based
on a set of rules" (Bonabeau 2002)
Models of occupant behavior: The challenge
The road ahead...
The heat balance paradigm

(no german abstract); engl. table of contents:
Understanding and modeling people's needs and behavior in buildings
Data input classes for performance simulation
Understanding and predicting buildings' performance
People in building performance simulation
Building performance simulation
Occupants' influence on building performance
Instances of passive effects
The issue of resolution
Representation of presence in simulation with discrete intervals
Instances of active effects
The spectrum of formalisms
Representation: states and events
The "gap" and the ensuant fallacies
Generation of random patterns
Plug load models - Use of office equipment
Case study of window operation
When use what model?
Single values versus distributions
The scope of data-driven models
A tradeoff
Cars' fuel economy as a comparative case in point
Another tradeoff
Cars' fuel economy as a comparative case in point
"I am sensitive to sensitivity analysis,
but uncertain about uncertainty analysis."
(keynote speech BS2019 Rome)
Building Performance Data/Indicators
Building performance prediction versus Building performance projection
Building Performance Data Ontology
The dimensions of model application space
When use what model?
"ABM represents decision-making entities, called agents
that individually assess their situation and make descision based
on a set of rules" (Bonabeau 2002)
Models of occupant behavior: The challenge
The road ahead...
The heat balance paradigm