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]


In the United States, the white share of the population is declining as Hispanic, Asian and black populations grow. But the shift to a more diverse nation is happening more quickly in some places than in others.

From 2000 to 2018, 109 counties in 22 states, from California to Kansas to North Carolina, went from majority white to majority nonwhite – that is, counties where non-Hispanic whites are no longer the majority, according to a Pew Research Center analysis of Census Bureau data. (Our analysis includes only counties with a minimum population of 10,000 in 2018. These counties represent 77% of the nation’s 3,142 counties and include 99% of the U.S. population.)

Overall, 293 U.S. counties were majority nonwhite in 2018. Most of these counties are concentrated in California, the South and on the East Coast, with few in the country’s middle section. In addition, several majority white counties with large populations may flip in coming years. Fairfax County, Virginia (total of 1.2 million), Pima County, Arizona (1 million), Milwaukee County, Wisconsin (948,000) and Cobb County, Georgia (757,000) all had populations that were less than 52% white.

In 21 of the 25 biggest U.S. counties by population, nonwhite groups together make up more than half of residents. Eight of these counties were majority white in 2000 but are no longer: San Diego, Orange, Riverside and Sacramento (all in California), plus Clark (Nevada), Broward (Florida), Tarrant (Texas) and Wayne (Michigan). Hispanics were the largest nonwhite population in all of these eight counties except Wayne – which contains Detroit – where the black population was the largest nonwhite group. (In Broward County, Hispanic and black residents made up similar shares of the population at 30% and 28%, respectively.)

As the nation’s racial and ethnic diversity grows, whites remain the single largest racial or ethnic group in the U.S. when looking at the country as a whole, accounting for 60% of all Americans. The four largest U.S. counties that had majority white populations in 2018 were Maricopa (Arizona), King (Washington), Middlesex (Massachusetts) and Palm Beach (Florida).

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Another way to highlight the nation’s changing demographics is to look at how many counties shifted the opposite way. From 2000 to 2018, just two counties went from minority white to majority white: Calhoun County in South Carolina and West Feliciana Parish in Louisiana, each with relatively small populations of about 15,000.

Among the 109 counties that between 2000 and 2018 shifted from majority white to majority nonwhite, 26 were at least 60% white in 2000. Counties in Georgia stand out for having five of the 10 biggest percentage point swings in their white population share. (These 10 counties also had the largest percentage point drop among all U.S. counties on this measure.)

Gwinnett County, near Atlanta, was largest in overall population (928,000) among these top 10. Its population dropped from 67% white in 2000 to 36% in 2018. Rockdale, home to 91,000 and also near Atlanta, had the biggest percentage point swing of the top 10 counties on this measure, falling from 73% white to 30% during the same time period.

This trend stems from a flat or declining number of white Americans in each of these five Georgia counties (Henry, Douglas and Newton are the other three), combined with a large and growing black population and a smaller Hispanic population that is also increasing in number. (In recent decades, many black Americans have moved to the Atlanta area from Northern states as part of a return migration to the South.)

The future racial and ethnic composition of the U.S. has been a subject of debate, due in part to the growing number of Americans with varied backgrounds – and how these Americans identify themselves. The number of multiracial Americans is rising, for example, and in a recent Pew Research Center survey, about half of U.S. Hispanic adults identified their race as white.

Correction: The chart “Counties where nonwhite share of population is above 50% are mostly in Southwest” has been updated to correct the color of seven counties.

Note: This is an update to a post originally published April 8, 2015.

For a closer look at counties that recently flipped, see the sortable table below:

U.S. counties where white share of population has dropped below 50% since 2000

State County Total population, 2000 Total population, 2018 % white, non-Hispanic, 2000 % white, non-Hispanic, 2018 Percentage point change, 2000-2018 AL Barbour 29,042 24,881 51% 46% -5% AL Jefferson 662,041 659,300 57% 50% -8% AL Russell 49,854 57,781 56% 46% -10% AK Kodiak Island 13,916 13,345 58% 49% -9% AR Crittenden 50,855 48,342 51% 41% -9% CA Contra Costa 948,865 1,150,215 59% 43% -15% CA Orange 2,846,282 3,185,968 52% 40% -12% CA Riverside 1,545,320 2,450,758 52% 35% -17% CA Sacramento 1,223,578 1,540,975 59% 44% -14% CA San Diego 2,813,839 3,343,364 56% 45% -10% CA San Mateo 707,099 769,545 51% 39% -12% CA Santa Barbara 399,293 446,527 57% 44% -13% CA Stanislaus 446,850 549,815 58% 41% -17% CA Sutter 78,947 96,807 61% 45% -15% CA Ventura 753,230 850,967 57% 45% -12% CA Yolo 168,660 220,408 59% 46% -13% CO Adams 348,076 511,868 63% 50% -13% CO Alamosa 14,965 16,683 55% 48% -6% FL Broward 1,622,974 1,951,260 59% 36% -23% FL Hardee 26,854 27,245 55% 47% -8% FL Hillsborough 998,994 1,436,888 64% 48% -15% FL Orange 896,123 1,380,645 58% 40% -18% FL Osceola 172,520 367,990 60% 31% -29% GA Chatham 232,372 289,195 54% 48% -6% GA Crisp 22,020 22,601 54% 50% -4% GA Decatur 28,252 26,575 56% 50% -6% GA Douglas 92,315 145,331 76% 39% -37% GA Gwinnett 588,133 927,781 67% 36% -31% GA Henry 119,498 230,220 80% 42% -38% GA Newton 61,906 109,541 74% 45% -29% GA Rockdale 70,292 90,594 73% 30% -43% GA Telfair 11,815 15,876 59% 49% -10% KS Finney 40,517 36,611 52% 40% -11% KS Ford 32,452 33,888 57% 39% -18% KS Wyandotte 157,924 165,324 52% 41% -11% LA Caddo 251,129 242,922 52% 45% -8% LA Claiborne 16,850 15,944 52% 45% -7% LA East Baton Rouge 412,899 440,956 55% 44% -11% LA Morehouse 31,037 25,398 56% 49% -7% LA St. John the Baptist 43,181 43,184 51% 34% -17% MD Charles 120,524 161,503 67% 39% -29% MD Montgomery 873,874 1,052,567 60% 43% -17% MA Suffolk 691,047 807,252 53% 45% -8% MI Wayne 2,061,180 1,753,893 51% 49% -1% MS Chickasaw 19,447 17,171 56% 49% -7% MS Leake 20,930 22,763 56% 47% -9% MS Panola 34,283 34,178 50% 47% -4% MS Pike 38,957 39,563 51% 43% -8% MS Scott 28,455 28,287 55% 49% -6% MS Warren 49,668 46,176 55% 47% -8% MS Winston 20,157 18,165 55% 50% -5% NE Colfax 10,434 10,881 73% 48% -25% NE Dakota 20,265 20,083 71% 48% -23% NV Clark 1,375,741 2,231,647 61% 42% -19% NJ Cumberland 146,454 150,972 59% 46% -13% NJ Mercer 350,773 369,811 65% 49% -16% NJ Middlesex 750,275 829,685 63% 42% -20% NJ Passaic 490,285 503,310 53% 41% -12% NJ Union 522,507 558,067 55% 40% -15% NM Chaves 61,373 64,689 52% 39% -14% NM Colfax 14,191 12,110 50% 47% -4% NM Curry 45,050 49,437 59% 47% -12% NM Eddy 51,633 57,900 58% 46% -12% NM Lea 55,528 69,611 54% 35% -19% NM Otero 62,195 66,781 57% 48% -8% NM Sandoval 90,546 145,179 51% 43% -8% NC Cumberland 302,978 332,330 53% 43% -10% NC Greene 18,957 21,012 50% 47% -3% NC Guilford 420,848 533,670 63% 50% -13% NC Lenoir 59,636 55,976 56% 49% -6% NC Mecklenburg 695,348 1,093,901 61% 46% -15% NC Nash 87,378 94,016 61% 49% -12% NC Scotland 35,952 34,810 51% 43% -8% NC Wilson 73,810 81,455 54% 47% -7% OK Cherokee 42,475 48,675 55% 47% -8% OK Texas 20,098 20,455 67% 44% -23% TX Andrews 13,002 18,128 57% 40% -17% TX Bell 238,000 355,642 58% 45% -13% TX Brazoria 241,805 370,200 66% 46% -19% TX Calhoun 20,645 21,561 52% 42% -11% TX Ector 121,124 162,124 51% 32% -20% TX Gonzales 18,601 20,826 51% 41% -10% TX Guadalupe 88,856 163,694 60% 50% -10% TX Hockley 22,719 22,980 58% 46% -12% TX Howard 33,630 36,459 57% 48% -9% TX Jefferson 251,968 255,001 52% 40% -12% TX Lamb 14,713 13,158 51% 39% -12% TX Matagorda 37,919 36,552 53% 44% -9% TX Medina 39,318 50,921 51% 43% -8% TX Midland 116,034 172,578 62% 45% -17% TX Moore 20,123 21,485 50% 31% -19% TX Potter 113,655 119,648 58% 43% -14% TX Tarrant 1,449,290 2,084,931 62% 46% -16% TX Terry 12,757 12,287 50% 39% -12% TX Titus 28,118 33,033 60% 44% -15% TX Travis 811,776 1,248,743 57% 49% -8% TX Victoria 84,077 92,035 53% 44% -9% TX Ward 10,909 11,720 52% 39% -13% TX Wharton 41,191 41,619 53% 44% -9% VA Prince William 281,317 468,011 65% 42% -23% VA Danville city 48,312 40,693 53% 42% -11% VA Hopewell city 22,278 22,596 61% 45% -16% VA Manassas city 34,692 41,641 67% 40% -27% VA Manassas Park city 10,226 17,307 68% 32% -36% VA Martinsville city 15,418 12,902 54% 44% -10% VA Newport News city 180,272 178,626 52% 43% -10% VA Suffolk city 63,699 91,185 53% 49% -4% WA Adams 16,426 19,759 51% 33% -18% WA Yakima 222,578 251,446 57% 43% -14%

Note: County list includes parishes (Louisiana) and a borough (Alaska).

Source: Pew Research Center analysis of 2000 decennial census and 2018 Census Bureau population estimates.