You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


/*
    Copyright 2005-2014 Intel Corporation.  All Rights Reserved.

    This file is part of Threading Building Blocks. Threading Building Blocks is free software;
    you can redistribute it and/or modify it under the terms of the GNU General Public License
    version 2  as  published  by  the  Free Software Foundation.  Threading Building Blocks is
    distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the
    implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
    See  the GNU General Public License for more details.   You should have received a copy of
    the  GNU General Public License along with Threading Building Blocks; if not, write to the
    Free Software Foundation, Inc.,  51 Franklin St,  Fifth Floor,  Boston,  MA 02110-1301 USA

    As a special exception,  you may use this file  as part of a free software library without
    restriction.  Specifically,  if other files instantiate templates  or use macros or inline
    functions from this file, or you compile this file and link it with other files to produce
    an executable,  this file does not by itself cause the resulting executable to be covered
    by the GNU General Public License. This exception does not however invalidate any other
    reasons why the executable file might be covered by the GNU General Public License.
*/

#ifndef __TBB_parallel_for_H
#define __TBB_parallel_for_H

#include <new>
#include "task.h"
#include "partitioner.h"
#include "blocked_range.h"
#include "tbb_exception.h"

namespace tbb {

namespace interface7 {
//! @cond INTERNAL
namespace internal {

    //! allocate right task with new parent
    void* allocate_sibling(task* start_for_task, size_t bytes);

    //! Task type used in parallel_for
    /** @ingroup algorithms */
    template<typename Range, typename Body, typename Partitioner>
    class start_for: public task {
        Range my_range;
        const Body my_body;
        typename Partitioner::task_partition_type my_partition;
        /*override*/ task* execute();

        //! Update affinity info, if any.
        /*override*/ void note_affinity( affinity_id id ) {
            my_partition.note_affinity( id );
        }

    public:
        //! Constructor for root task.
        start_for( const Range& range, const Body& body, Partitioner& partitioner ) :
            my_range(range),
            my_body(body),
            my_partition(partitioner)
        {
        }
        //! Splitting constructor used to generate children.
        /** parent_ becomes left child.  Newly constructed object is right child. */
        start_for( start_for& parent_, typename Partitioner::split_type& split_obj) :
            my_range(parent_.my_range, split_obj),
            my_body(parent_.my_body),
            my_partition(parent_.my_partition, split_obj)
        {
            my_partition.set_affinity(*this);
        }
        //! Construct right child from the given range as response to the demand.
        /** parent_ remains left child.  Newly constructed object is right child. */
        start_for( start_for& parent_, const Range& r, depth_t d ) :
            my_range(r),
            my_body(parent_.my_body),
            my_partition(parent_.my_partition, split())
        {
            my_partition.set_affinity(*this);
            my_partition.align_depth( d );
        }
        static void run(  const Range& range, const Body& body, Partitioner& partitioner ) {
            if( !range.empty() ) {
#if !__TBB_TASK_GROUP_CONTEXT || TBB_JOIN_OUTER_TASK_GROUP
                start_for& a = *new(task::allocate_root()) start_for(range,body,partitioner);
#else
                // Bound context prevents exceptions from body to affect nesting or sibling algorithms,
                // and allows users to handle exceptions safely by wrapping parallel_for in the try-block.
                task_group_context context;
                start_for& a = *new(task::allocate_root(context)) start_for(range,body,partitioner);
#endif /* __TBB_TASK_GROUP_CONTEXT && !TBB_JOIN_OUTER_TASK_GROUP */
                task::spawn_root_and_wait(a);
            }
        }
#if __TBB_TASK_GROUP_CONTEXT
        static void run(  const Range& range, const Body& body, Partitioner& partitioner, task_group_context& context ) {
            if( !range.empty() ) {
                start_for& a = *new(task::allocate_root(context)) start_for(range,body,partitioner);
                task::spawn_root_and_wait(a);
            }
        }
#endif /* __TBB_TASK_GROUP_CONTEXT */
        //! Run body for range, serves as callback for partitioner
        void run_body( Range &r ) { my_body( r ); }

        //! spawn right task, serves as callback for partitioner
        void offer_work(typename Partitioner::split_type& split_obj) {
            spawn( *new( allocate_sibling(static_cast<task*>(this), sizeof(start_for)) ) start_for(*this, split_obj) );
        }
        //! spawn right task, serves as callback for partitioner
        void offer_work(const Range& r, depth_t d = 0) {
            spawn( *new( allocate_sibling(static_cast<task*>(this), sizeof(start_for)) ) start_for(*this, r, d) );
        }
    };

    //! allocate right task with new parent
    // TODO: 'inline' here is to avoid multiple definition error but for sake of code size this should not be inlined
    inline void* allocate_sibling(task* start_for_task, size_t bytes) {
        task* parent_ptr = new( start_for_task->allocate_continuation() ) flag_task();
        start_for_task->set_parent(parent_ptr);
        parent_ptr->set_ref_count(2);
        return &parent_ptr->allocate_child().allocate(bytes);
    }

    //! execute task for parallel_for
    template<typename Range, typename Body, typename Partitioner>
    task* start_for<Range,Body,Partitioner>::execute() {
        my_partition.check_being_stolen( *this );
        my_partition.execute(*this, my_range);
        return NULL;
    }
} // namespace internal
//! @endcond
} // namespace interfaceX

//! @cond INTERNAL
namespace internal {
    using interface7::internal::start_for;

    //! Calls the function with values from range [begin, end) with a step provided
    template<typename Function, typename Index>
    class parallel_for_body : internal::no_assign {
        const Function &my_func;
        const Index my_begin;
        const Index my_step;
    public:
        parallel_for_body( const Function& _func, Index& _begin, Index& _step )
            : my_func(_func), my_begin(_begin), my_step(_step) {}

        void operator()( const tbb::blocked_range<Index>& r ) const {
            // A set of local variables to help the compiler with vectorization of the following loop.
            Index b = r.begin();
            Index e = r.end();
            Index ms = my_step;
            Index k = my_begin + b*ms;

#if __INTEL_COMPILER
#pragma ivdep
#if __TBB_ASSERT_ON_VECTORIZATION_FAILURE
#pragma vector always assert
#endif
#endif
            for ( Index i = b; i < e; ++i, k += ms ) {
                my_func( k );
            }
        }
    };
} // namespace internal
//! @endcond

// Requirements on Range concept are documented in blocked_range.h

/** \page parallel_for_body_req Requirements on parallel_for body
    Class \c Body implementing the concept of parallel_for body must define:
    - \code Body::Body( const Body& ); \endcode                 Copy constructor
    - \code Body::~Body(); \endcode                             Destructor
    - \code void Body::operator()( Range& r ) const; \endcode   Function call operator applying the body to range \c r.
**/

/** \name parallel_for
    See also requirements on \ref range_req "Range" and \ref parallel_for_body_req "parallel_for Body". **/
//@{

//! Parallel iteration over range with default partitioner.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body ) {
    internal::start_for<Range,Body,const __TBB_DEFAULT_PARTITIONER>::run(range,body,__TBB_DEFAULT_PARTITIONER());
}

//! Parallel iteration over range with simple partitioner.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, const simple_partitioner& partitioner ) {
    internal::start_for<Range,Body,const simple_partitioner>::run(range,body,partitioner);
}

//! Parallel iteration over range with auto_partitioner.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, const auto_partitioner& partitioner ) {
    internal::start_for<Range,Body,const auto_partitioner>::run(range,body,partitioner);
}

//! Parallel iteration over range with affinity_partitioner.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, affinity_partitioner& partitioner ) {
    internal::start_for<Range,Body,affinity_partitioner>::run(range,body,partitioner);
}

#if __TBB_TASK_GROUP_CONTEXT
//! Parallel iteration over range with default partitioner and user-supplied context.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, task_group_context& context ) {
    internal::start_for<Range,Body,const __TBB_DEFAULT_PARTITIONER>::run(range, body, __TBB_DEFAULT_PARTITIONER(), context);
}

//! Parallel iteration over range with simple partitioner and user-supplied context.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, const simple_partitioner& partitioner, task_group_context& context ) {
    internal::start_for<Range,Body,const simple_partitioner>::run(range, body, partitioner, context);
}

//! Parallel iteration over range with auto_partitioner and user-supplied context.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, const auto_partitioner& partitioner, task_group_context& context ) {
    internal::start_for<Range,Body,const auto_partitioner>::run(range, body, partitioner, context);
}

//! Parallel iteration over range with affinity_partitioner and user-supplied context.
/** @ingroup algorithms **/
template<typename Range, typename Body>
void parallel_for( const Range& range, const Body& body, affinity_partitioner& partitioner, task_group_context& context ) {
    internal::start_for<Range,Body,affinity_partitioner>::run(range,body,partitioner, context);
}
#endif /* __TBB_TASK_GROUP_CONTEXT */
//@}

namespace strict_ppl {

//@{
//! Implementation of parallel iteration over stepped range of integers with explicit step and partitioner
template <typename Index, typename Function, typename Partitioner>
void parallel_for_impl(Index first, Index last, Index step, const Function& f, Partitioner& partitioner) {
    if (step <= 0 )
        internal::throw_exception(internal::eid_nonpositive_step); // throws std::invalid_argument
    else if (last > first) {
        // Above "else" avoids "potential divide by zero" warning on some platforms
        Index end = (last - first - Index(1)) / step + Index(1);
        tbb::blocked_range<Index> range(static_cast<Index>(0), end);
        internal::parallel_for_body<Function, Index> body(f, first, step);
        tbb::parallel_for(range, body, partitioner);
    }
}

//! Parallel iteration over a range of integers with a step provided and default partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, step, f, auto_partitioner());
}
//! Parallel iteration over a range of integers with a step provided and simple partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, const simple_partitioner& partitioner) {
    parallel_for_impl<Index,Function,const simple_partitioner>(first, last, step, f, partitioner);
}
//! Parallel iteration over a range of integers with a step provided and auto partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, const auto_partitioner& partitioner) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, step, f, partitioner);
}
//! Parallel iteration over a range of integers with a step provided and affinity partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, affinity_partitioner& partitioner) {
    parallel_for_impl(first, last, step, f, partitioner);
}

//! Parallel iteration over a range of integers with a default step value and default partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, const Function& f) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, static_cast<Index>(1), f, auto_partitioner());
}
//! Parallel iteration over a range of integers with a default step value and simple partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, const Function& f, const simple_partitioner& partitioner) {
    parallel_for_impl<Index,Function,const simple_partitioner>(first, last, static_cast<Index>(1), f, partitioner);
}
//! Parallel iteration over a range of integers with a default step value and auto partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, const Function& f, const auto_partitioner& partitioner) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, static_cast<Index>(1), f, partitioner);
}
//! Parallel iteration over a range of integers with a default step value and affinity partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, const Function& f, affinity_partitioner& partitioner) {
    parallel_for_impl(first, last, static_cast<Index>(1), f, partitioner);
}

#if __TBB_TASK_GROUP_CONTEXT
//! Implementation of parallel iteration over stepped range of integers with explicit step, task group context, and partitioner
template <typename Index, typename Function, typename Partitioner>
void parallel_for_impl(Index first, Index last, Index step, const Function& f, Partitioner& partitioner, tbb::task_group_context &context) {
    if (step <= 0 )
        internal::throw_exception(internal::eid_nonpositive_step); // throws std::invalid_argument
    else if (last > first) {
        // Above "else" avoids "potential divide by zero" warning on some platforms
        Index end = (last - first - Index(1)) / step + Index(1);
        tbb::blocked_range<Index> range(static_cast<Index>(0), end);
        internal::parallel_for_body<Function, Index> body(f, first, step);
        tbb::parallel_for(range, body, partitioner, context);
    }
}

//! Parallel iteration over a range of integers with explicit step, task group context, and default partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, tbb::task_group_context &context) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, step, f, auto_partitioner(), context);
}
//! Parallel iteration over a range of integers with explicit step, task group context, and simple partitioner
 template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, const simple_partitioner& partitioner, tbb::task_group_context &context) {
    parallel_for_impl<Index,Function,const simple_partitioner>(first, last, step, f, partitioner, context);
}
//! Parallel iteration over a range of integers with explicit step, task group context, and auto partitioner
 template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, const auto_partitioner& partitioner, tbb::task_group_context &context) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, step, f, partitioner, context);
}
//! Parallel iteration over a range of integers with explicit step, task group context, and affinity partitioner
 template <typename Index, typename Function>
void parallel_for(Index first, Index last, Index step, const Function& f, affinity_partitioner& partitioner, tbb::task_group_context &context) {
    parallel_for_impl(first, last, step, f, partitioner, context);
}


//! Parallel iteration over a range of integers with a default step value, explicit task group context, and default partitioner
template <typename Index, typename Function>
void parallel_for(Index first, Index last, const Function& f, tbb::task_group_context &context) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, static_cast<Index>(1), f, auto_partitioner(), context);
}
//! Parallel iteration over a range of integers with a default step value, explicit task group context, and simple partitioner
 template <typename Index, typename Function, typename Partitioner>
void parallel_for(Index first, Index last, const Function& f, const simple_partitioner& partitioner, tbb::task_group_context &context) {
    parallel_for_impl<Index,Function,const simple_partitioner>(first, last, static_cast<Index>(1), f, partitioner, context);
}
//! Parallel iteration over a range of integers with a default step value, explicit task group context, and auto partitioner
 template <typename Index, typename Function, typename Partitioner>
void parallel_for(Index first, Index last, const Function& f, const auto_partitioner& partitioner, tbb::task_group_context &context) {
    parallel_for_impl<Index,Function,const auto_partitioner>(first, last, static_cast<Index>(1), f, partitioner, context);
}
//! Parallel iteration over a range of integers with a default step value, explicit task group context, and affinity_partitioner
 template <typename Index, typename Function, typename Partitioner>
void parallel_for(Index first, Index last, const Function& f, affinity_partitioner& partitioner, tbb::task_group_context &context) {
    parallel_for_impl(first, last, static_cast<Index>(1), f, partitioner, context);
}

#endif /* __TBB_TASK_GROUP_CONTEXT */
//@}

} // namespace strict_ppl

using strict_ppl::parallel_for;

} // namespace tbb

#if TBB_PREVIEW_SERIAL_SUBSET
#define __TBB_NORMAL_EXECUTION
#include "../serial/tbb/parallel_for.h"
#undef __TBB_NORMAL_EXECUTION
#endif

#endif /* __TBB_parallel_for_H */